Paper Digest: EMNLP 2021 Highlights
Download EMNLP-2021-Paper-Digests.pdf– Highlights of all EMNLP-2021 papers (Main Track). Readers can choose to read all these highlights on our console, which allows users to filter out papers using keywords and find related papers, patents, etc. You may also like to explore our “Best Paper” Digest (EMNLP), which lists the most influential EMNLP papers in the last 25 years.
The Conference on Empirical Methods in Natural Language Processing (EMNLP) is one of the top natural language processing conferences in the world. In 2021, it will be held both online and in Punta Cana, Dominican Republic.
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TABLE 1: Paper Digest: EMNLP 2021 Highlights
| Paper | Author(s) | Code | |
|---|---|---|---|
| 1 | AligNART: Non-autoregressive Neural Machine Translation By Jointly Learning to Estimate Alignment and Translate Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality of the target distribution. |
Jongyoon Song; Sungwon Kim; Sungroh Yoon; | |
| 2 | Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on a zero-shot cross-lingual transfer task in NMT. |
Guanhua Chen; Shuming Ma; Yun Chen; Li Dong; Dongdong Zhang; Jia Pan; Wenping Wang; Furu Wei; | |
| 3 | ERNIE-M: Enhanced Multilingual Representation By Aligning Cross-lingual Semantics with Monolingual Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Ernie-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. |
Xuan Ouyang; Shuohuan Wang; Chao Pang; Yu Sun; Hao Tian; Hua Wu; Haifeng Wang; | |
| 4 | Cross Attention Augmented Transducer Networks for Simultaneous Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. |
Dan Liu; Mengge Du; Xiaoxi Li; Ya Li; Enhong Chen; | |
| 5 | Translating Headers of Tabular Data: A Pilot Study of Schema Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese. Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context. |
Kunrui Zhu; Yan Gao; Jiaqi Guo; Jian-Guang Lou; | code |
| 6 | Towards Making The Most of Dialogue Characteristics for Neural Chat Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. |
Yunlong Liang; Chulun Zhou; Fandong Meng; Jinan Xu; Yufeng Chen; Jinsong Su; Jie Zhou; | |
| 7 | Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the gap between out-of-domain pretraining and in-domain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data. |
Yicheng Zou; Bolin Zhu; Xingwu Hu; Tao Gui; Qi Zhang; | |
| 8 | Controllable Neural Dialogue Summarization with Personal Named Entity Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. |
Zhengyuan Liu; Nancy Chen; | |
| 9 | Fine-grained Factual Consistency Assessment for Abstractive Summarization Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a fine-grained two-stage Fact Consistency assessment framework for Summarization models (SumFC). |
Sen Zhang; Jianwei Niu; Chuyuan Wei; | |
| 10 | Decision-Focused Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision. |
Chao-Chun Hsu; Chenhao Tan; | |
| 11 | Multiplex Graph Neural Network for Extractive Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. |
Baoyu Jing; Zeyu You; Tao Yang; Wei Fan; Hanghang Tong; | |
| 12 | A Thorough Evaluation of Task-Specific Pretraining for Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We compare three summarization specific pretraining objectives with the task agnostic corrupted span prediction pretraining in controlled study. |
Sascha Rothe; Joshua Maynez; Shashi Narayan; | |
| 13 | HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate these issues, this paper proposes HetFormer, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. |
Ye Liu; Jianguo Zhang; Yao Wan; Congying Xia; Lifang He; Philip Yu; | |
| 14 | Unsupervised Keyphrase Extraction By Jointly Modeling Local and Global Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. |
Xinnian Liang; Shuangzhi Wu; Mu Li; Zhoujun Li; | |
| 15 | Distantly Supervised Relation Extraction Using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of word-level noise by emphasizing inner-sentence correlations before extracting relevant information within sentences. |
Xiangyu Lin; Tianyi Liu; Weijia Jia; Zhiguo Gong; | |
| 16 | Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). |
Qi Shi; Yu Zhang; Qingyu Yin; Ting Liu; | |
| 17 | A Partition Filter Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. |
Zhiheng Yan; Chong Zhang; Jinlan Fu; Qi Zhang; Zhongyu Wei; | code |
| 18 | TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. |
Zheng Fang; Yanan Cao; Tai Li; Ruipeng Jia; Fang Fang; Yanmin Shang; Yuhai Lu; | |
| 19 | Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. |
Bin Liang; Hang Su; Rongdi Yin; Lin Gui; Min Yang; Qin Zhao; Xiaoqi Yu; Ruifeng Xu; | |
| 20 | DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with An Application to Aspect Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new fine-tuning scheme for BERT, which aims to address the above challenges. |
Entony Lekhtman; Yftah Ziser; Roi Reichart; | |
| 21 | Improving Multimodal Fusion Via Mutual Dependency Maximisation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities. |
Pierre Colombo; Emile Chapuis; Matthieu Labeau; Chlo? Clavel; | |
| 22 | Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this issue, we adopt Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources. |
Zhengyan Li; Yicheng Zou; Chong Zhang; Qi Zhang; Zhongyu Wei; | |
| 23 | Progressive Self-Training with Discriminator for Aspect Term Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we use two means to alleviate the noise in the pseudo-labels. |
Qianlong Wang; Zhiyuan Wen; Qin Zhao; Min Yang; Ruifeng Xu; | |
| 24 | Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an end-to-end reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. |
Hao Chen; Rui Xia; Jianfei Yu; | |
| 25 | Idiosyncratic But Not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive Yet Consistent Individual Styles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new approach to studying idiolects through a massive cross-author comparison to identify and encode stylistic features. |
Jian Zhu; David Jurgens; | |
| 26 | Narrative Theory for Computational Narrative Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications. |
Andrew Piper; Richard Jean So; David Bamman; | |
| 27 | (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures. |
Kenneth Joseph; Sarah Shugars; Ryan Gallagher; Jon Green; Alexi Quintana Math?; Zijian An; David Lazer; | |
| 28 | How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the benefits of CAD for social NLP models by focusing on three social computing constructs – sentiment, sexism, and hate speech. |
Indira Sen; Mattia Samory; Fabian Fl?ck; Claudia Wagner; Isabelle Augenstein; | |
| 29 | Latent Hatred: A Benchmark for Understanding Implicit Hate Speech Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, this work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. |
Mai ElSherief; Caleb Ziems; David Muchlinski; Vaishnavi Anupindi; Jordyn Seybolt; Munmun De Choudhury; Diyi Yang; | |
| 30 | Distilling Linguistic Context for Language Model Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation. |
Geondo Park; Gyeongman Kim; Eunho Yang; | |
| 31 | Dynamic Knowledge Distillation for Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore whether a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency, regarding the student performance and learning efficiency. |
Lei Li; Yankai Lin; Shuhuai Ren; Peng Li; Jie Zhou; Xu Sun; | |
| 32 | Few-Shot Text Generation with Natural Language Instructions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show how these challenges can be tackled: We introduce GenPET, a method for text generation that is based on pattern-exploiting training, a recent approach for combining textual instructions with supervised learning that only works for classification tasks. |
Timo Schick; Hinrich Sch?tze; | |
| 33 | SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. |
Kangli Zi; Shi Wang; Yu Liu; Jicun Li; Yanan Cao; Cungen Cao; | |
| 34 | Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data By Feature Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this paper focuses on addressing these problems and proposes a time-efficient sampling method to select the data that is most relevant to the primary task. |
Po-Nien Kung; Sheng-Siang Yin; Yi-Cheng Chen; Tse-Hsuan Yang; Yun-Nung Chen; | |
| 35 | GOLD: Improving Out-of-Scope Detection in Dialogues Using Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. |
Derek Chen; Zhou Yu; | |
| 36 | Graph Based Network with Contextualized Representations of Turns in Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. |
Bongseok Lee; Yong Suk Choi; | code |
| 37 | Automatically Exposing Problems with Neural Dialog Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. |
Dian Yu; Kenji Sagae; | |
| 38 | Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and high-quality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref. |
Michael Bugert; Iryna Gurevych; | |
| 39 | Inducing Stereotypical Character Roles from Plot Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a fully unsupervised k-means clustering approach for learning stereotypical roles given only structural plot information. |
Labiba Jahan; Rahul Mittal; Mark Finlayson; | |
| 40 | Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. |
Alexander Spangher; Jonathan May; Sz-Rung Shiang; Lingjia Deng; | |
| 41 | Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias in BERT, GPT-2, T5, and XLNet. |
Robert Wolfe; Aylin Caliskan; | |
| 42 | Mitigating Language-Dependent Ethnic Bias in BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. |
Jaimeen Ahn; Alice Oh; | |
| 43 | Adversarial Scrubbing of Demographic Information for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an adversarial learning framework Adversarial Scrubber (AdS), to debias contextual representations. |
Somnath Basu Roy Chowdhury; Sayan Ghosh; Yiyuan Li; Junier Oliva; Shashank Srivastava; Snigdha Chaturvedi; | |
| 44 | Open-domain Clarification Question Generation Without Question Examples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. |
Julia White; Gabriel Poesia; Robert Hawkins; Dorsa Sadigh; Noah Goodman; | |
| 45 | Improving Sequence-to-Sequence Pre-training Via Sequence Span Rewriting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Sequence Span Rewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training. |
Wangchunshu Zhou; Tao Ge; Canwen Xu; Ke Xu; Furu Wei; | |
| 46 | Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. |
Dheeraj Mekala; Varun Gangal; Jingbo Shang; | |
| 47 | Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions as queries. |
Carl Edwards; ChengXiang Zhai; Heng Ji; | |
| 48 | Classification of Hierarchical Text Using Geometric Deep Learning: The Case of Clinical Trials Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. |
Sohrab Ferdowsi; Nikolay Borissov; Julien Knafou; Poorya Amini; Douglas Teodoro; | |
| 49 | The Devil Is in The Detail: Simple Tricks Improve Systematic Generalization of Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we demonstrate that by revisiting model configurations as basic as scaling of embeddings, early stopping, relative positional embedding, and Universal Transformer variants, we can drastically improve the performance of Transformers on systematic generalization. |
R?bert Csord?s; Kazuki Irie; Juergen Schmidhuber; | |
| 50 | Artificial Text Detection Via Examining The Topology of Attention Maps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) which is currently understudied in the field of NLP. |
Laida Kushnareva; Daniil Cherniavskii; Vladislav Mikhailov; Ekaterina Artemova; Serguei Barannikov; Alexander Bernstein; Irina Piontkovskaya; Dmitri Piontkovski; Evgeny Burnaev; | |
| 51 | Active Learning By Acquiring Contrastive Examples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. |
Katerina Margatina; Giorgos Vernikos; Lo?c Barrault; Nikolaos Aletras; | |
| 52 | Conditional Poisson Stochastic Beams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search. |
Clara Meister; Afra Amini; Tim Vieira; Ryan Cotterell; | |
| 53 | Building Adaptive Acceptability Classifiers for Neural NLG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches. |
Soumya Batra; Shashank Jain; Peyman Heidari; Ankit Arun; Catharine Youngs; Xintong Li; Pinar Donmez; Shawn Mei; Shiunzu Kuo; Vikas Bhardwaj; Anuj Kumar; Michael White; | |
| 54 | Moral Stories: Situated Reasoning About Norms, Intents, Actions, and Their Consequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goal-oriented social reasoning. |
Denis Emelin; Ronan Le Bras; Jena D. Hwang; Maxwell Forbes; Yejin Choi; | |
| 55 | Truth-Conditional Captions for Time Series Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. |
Harsh Jhamtani; Taylor Berg-Kirkpatrick; | |
| 56 | Injecting Entity Types Into Entity-Guided Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. |
Xiangyu Dong; Wenhao Yu; Chenguang Zhu; Meng Jiang; | |
| 57 | Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. |
Leonardo F. R. Ribeiro; Jonas Pfeiffer; Yue Zhang; Iryna Gurevych; | |
| 58 | Learning Compact Metrics for MT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a series of experiments which show that model size is indeed a bottleneck for cross-lingual transfer, then demonstrate how distillation can help addressing this bottleneck, by leveraging synthetic data generation and transferring knowledge from one teacher to multiple students trained on related languages. |
Amy Pu; Hyung Won Chung; Ankur Parikh; Sebastian Gehrmann; Thibault Sellam; | |
| 59 | The Impact of Positional Encodings on Multilingual Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first show that surprisingly, while these modifications tend to improve monolingual language models, none of them result in better multilingual language models. We then answer why that is: sinusoidal encodings were explicitly designed to facilitate compositionality by allowing linear projections over arbitrary time steps. |
Vinit Ravishankar; Anders S?gaard; | |
| 60 | Disentangling Representations of Text By Masking Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrained models that encode distinct, complementary aspects. |
Xiongyi Zhang; Jan-Willem van de Meent; Byron Wallace; | |
| 61 | Exploring The Role of BERT Token Representations to Explain Sentence Probing Results Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results. |
Hosein Mohebbi; Ali Modarressi; Mohammad Taher Pilehvar; | |
| 62 | Do Long-Range Language Models Actually Use Long-Range Context? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we perform a fine-grained analysis of two long-range Transformer language models (including the Routing Transformer, which achieves state-of-the-art perplexity on the PG-19 long-sequence LM benchmark dataset) that accept input sequences of up to 8K tokens. |
Simeng Sun; Kalpesh Krishna; Andrew Mattarella-Micke; Mohit Iyyer; | |
| 63 | The World of An Octopus: How Reporting Bias Influences A Language Model’s Perception of Color Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first demonstrate that reporting bias, the tendency of people to not state the obvious, is one of the causes of this limitation, and then investigate to what extent multimodal training can mitigate this issue. |
Cory Paik; St?phane Aroca-Ouellette; Alessandro Roncone; Katharina Kann; | |
| 64 | SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. |
Dheeraj Rajagopal; Vidhisha Balachandran; Eduard H Hovy; Yulia Tsvetkov; | |
| 65 | Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. |
David Wilmot; Frank Keller; | |
| 66 | Semantic Novelty Detection in Natural Language Descriptions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example. |
Nianzu Ma; Alexander Politowicz; Sahisnu Mazumder; Jiahua Chen; Bing Liu; Eric Robertson; Scott Grigsby; | |
| 67 | Jump-Starting Item Parameters for Adaptive Language Tests Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While prior work has addressed ‘cold start’ estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-start these estimates, rapidly improving their quality with as few as 500 test-takers and a small sample of item exposures (~6 each) from a large item bank (~4,000 items). |
Arya D. McCarthy; Kevin P. Yancey; Geoff T. LaFlair; Jesse Egbert; Manqian Liao; Burr Settles; | |
| 68 | Voice Query Auto Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. |
Raphael Tang; Karun Kumar; Kendra Chalkley; Ji Xin; Liming Zhang; Wenyan Li; Gefei Yang; Yajie Mao; Junho Shin; Geoffrey Craig Murray; Jimmy Lin; | |
| 69 | CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. |
Mat?? Falis; Hang Dong; Alexandra Birch; Beatrice Alex; | |
| 70 | Learning Universal Authorship Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. |
Rafael A. Rivera-Soto; Olivia Elizabeth Miano; Juanita Ordonez; Barry Y. Chen; Aleem Khan; Marcus Bishop; Nicholas Andrews; | |
| 71 | Predicting Emergent Linguistic Compositions Through Time: Syntactic Frame Extension Via Multimodal Chaining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the syntactic frame extension model (SFEM) that draws on the theory of chaining and knowledge from percept, concept, and language to infer how verbs extend their frames to form new compositions with existing and novel nouns. |
Lei Yu; Yang Xu; | |
| 72 | Frequency Effects on Syntactic Rule Learning in Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules. We investigate this question using the case study of BERT’s performance on English subject-verb agreement. |
Jason Wei; Dan Garrette; Tal Linzen; Ellie Pavlick; | |
| 73 | A Surprisal-duration Trade-off Across and Within The World’s Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa. |
Tiago Pimentel; Clara Meister; Elizabeth Salesky; Simone Teufel; Dami?n Blasi; Ryan Cotterell; | |
| 74 | Revisiting The Uniform Information Density Hypothesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we investigate these facets of the UID hypothesis using reading time and acceptability data. |
Clara Meister; Tiago Pimentel; Patrick Haller; Lena J?ger; Ryan Cotterell; Roger Levy; | |
| 75 | Condenser: A Pre-training Architecture for Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. |
Luyu Gao; Jamie Callan; | |
| 76 | Monitoring Geometrical Properties of Word Embeddings for Detecting The Emergence of New Topics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we tackle the problem of early detection of slowly emerging new topics. |
Cl?ment Christophe; Julien Velcin; Jairo Cugliari; Manel Boumghar; Philippe Suignard; | |
| 77 | Contextualized Query Embeddings for Conversational Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations. |
Sheng-Chieh Lin; Jheng-Hong Yang; Jimmy Lin; | |
| 78 | Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking the merits of the sparse and dense representations, we propose an ultra-high dimensional (UHD) representation scheme equipped with directly controllable sparsity. |
Kyoung-Rok Jang; Junmo Kang; Giwon Hong; Sung-Hyon Myaeng; Joohee Park; Taewon Yoon; Heecheol Seo; | |
| 79 | IR Like A SIR: Sense-enhanced Information Retrieval for Multiple Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. |
Rexhina Blloshmi; Tommaso Pasini; Niccol? Campolungo; Somnath Banerjee; Roberto Navigli; Gabriella Pasi; | code |
| 80 | Neural Attention-Aware Hierarchical Topic Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. |
Yuan Jin; He Zhao; Ming Liu; Lan Du; Wray Buntine; | |
| 81 | Relational World Knowledge Representation in Contextual Language Models: A Review Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this review, we take a natural language processing perspective to these limitations, examining how they may be addressed in part by training deep contextual language models (LMs) to internalize and express relational knowledge in more flexible forms. |
Tara Safavi; Danai Koutra; | |
| 82 | Certified Robustness to Programmable Transformations in LSTMs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an approach to certifying the robustness of LSTMs (and extensions of LSTMs) and training models that can be efficiently certified. |
Yuhao Zhang; Aws Albarghouthi; Loris D?Antoni; | |
| 83 | ReGen: Reinforcement Learning for Text and Knowledge Base Generation Using Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning to improve performance. |
Pierre Dognin; Inkit Padhi; Igor Melnyk; Payel Das; | code |
| 84 | Contrastive Out-of-Distribution Detection for Pretrained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. |
Wenxuan Zhou; Fangyu Liu; Muhao Chen; | |
| 85 | MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. |
Cristian-Paul Bara; Sky CH-Wang; Joyce Chai; | |
| 86 | Detecting Speaker Personas from Conversational Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. |
Jia-Chen Gu; Zhenhua Ling; Yu Wu; Quan Liu; Zhigang Chen; Xiaodan Zhu; | |
| 87 | Cross-lingual Intermediate Fine-tuning Improves Dialogue State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks. |
Nikita Moghe; Mark Steedman; Alexandra Birch; | |
| 88 | ConvFiT: Conversational Fine-Tuning of Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ConvFiT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 ConvFiT-ing) and task-specialised sentence encoder (after Stage 2). |
Ivan Vulic; Pei-Hao Su; Samuel Coope; Daniela Gerz; Pawel Budzianowski; I?igo Casanueva; Nikola Mrk?ic; Tsung-Hsien Wen; | |
| 89 | We’ve Had This Conversation Before: A Novel Approach to Measuring Dialog Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. |
Ofer Lavi; Ella Rabinovich; Segev Shlomov; David Boaz; Inbal Ronen; Ateret Anaby Tavor; | |
| 90 | Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine the feasibility of LT for incremental NLU in English. |
Patrick Kahardipraja; Brielen Madureira; David Schlangen; | |
| 91 | Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the feedback attribution problem that arises when using counterfactual bandit learning for multi-domain spoken language understanding. |
Tobias Falke; Patrick Lehnen; | |
| 92 | Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. |
Oscar Sainz; Oier Lopez de Lacalle; Gorka Labaka; Ander Barrena; Eneko Agirre; | |
| 93 | Extend, Don’t Rebuild: Phrasing Conditional Graph Modification As Autoregressive Sequence Labelling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation. |
Leon Weber; Jannes M?nchmeyer; Samuele Garda; Ulf Leser; | |
| 94 | Zero-Shot Information Extraction As A Unified Text-to-Triple Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We cast a suite of information extraction tasks into a text-to-triple translation framework. |
Chenguang Wang; Xiao Liu; Zui Chen; Haoyun Hong; Jie Tang; Dawn Song; | |
| 95 | Learning Logic Rules for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. |
Dongyu Ru; Changzhi Sun; Jiangtao Feng; Lin Qiu; Hao Zhou; Weinan Zhang; Yong Yu; Lei Li; | code |
| 96 | A Large-Scale Dataset for Empathetic Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category. |
Anuradha Welivita; Yubo Xie; Pearl Pu; | |
| 97 | The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first conduct a survey of 45 open-ended text generation papers and find that the vast majority of them fail to report crucial details about their AMT tasks, hindering reproducibility. We then run a series of story evaluation experiments with both AMT workers and English teachers and discover that even with strict qualification filters, AMT workers (unlike teachers) fail to distinguish between model-generated text and human-generated references. |
Marzena Karpinska; Nader Akoury; Mohit Iyyer; | |
| 98 | Documenting Large Webtext Corpora: A Case Study on The Colossal Clean Crawled Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. |
Jesse Dodge; Maarten Sap; Ana Marasovic; William Agnew; Gabriel Ilharco; Dirk Groeneveld; Margaret Mitchell; Matt Gardner; | |
| 99 | AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we propose AfroMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. |
Machel Reid; Junjie Hu; Graham Neubig; Yutaka Matsuo; | |
| 100 | Evaluating The Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we evaluate leading automatic metrics on the oft-researched task of formality style transfer. |
Eleftheria Briakou; Sweta Agrawal; Joel Tetreault; Marine Carpuat; | |
| 101 | MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task. |
Tim O?Gorman; Zach Jensen; Sheshera Mysore; Kevin Huang; Rubayyat Mahbub; Elsa Olivetti; Andrew McCallum; | |
| 102 | Understanding Politics Via Contextualized Discourse Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. |
Rajkumar Pujari; Dan Goldwasser; | |
| 103 | Conundrums in Event Coreference Resolution: Making Sense of The State of The Art Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NLP audience with a better understanding of the state of the art and reference researchers with directions for future research. |
Jing Lu; Vincent Ng; | |
| 104 | Weakly Supervised Discourse Segmentation for Multiparty Oral Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. |
Lila Gravellier; Julie Hunter; Philippe Muller; Thomas Pellegrini; Isabelle Ferran?; | |
| 105 | Narrative Embedding: Re-Contextualization Through Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel approach for narrative event representation using attention to re-contextualize events across the whole story. |
Sean Wilner; Daniel Woolridge; Madeleine Glick; | |
| 106 | Focus on What Matters: Applying Discourse Coherence Theory to Cross Document Coreference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We model the entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features. |
William Held; Dan Iter; Dan Jurafsky; | |
| 107 | Salience-Aware Event Chain Modeling for Narrative Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. |
Xiyang Zhang; Muhao Chen; Jonathan May; | |
| 108 | Asking It All: Generating Contextualized Questions for Any Semantic Role Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. |
Valentina Pyatkin; Paul Roit; Julian Michael; Yoav Goldberg; Reut Tsarfaty; Ido Dagan; | |
| 109 | Fast, Effective, and Self-Supervised: Transforming Masked Language Models Into Universal Lexical and Sentence Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that it is possible to turn MLMs into effective lexical and sentence encoders even without any additional data, relying simply on self-supervision. |
Fangyu Liu; Ivan Vulic; Anna Korhonen; Nigel Collier; | |
| 110 | RuleBERT: Teaching Soft Rules to Pre-Trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we aim to bridge this gap by teaching PLMs how to reason with soft Horn rules. |
Mohammed Saeed; Naser Ahmadi; Preslav Nakov; Paolo Papotti; | |
| 111 | Stepmothers Are Mean and Academics Are Pretentious: What Do Pretrained Language Models Learn About You? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate what types of stereotypical information are captured by pretrained language models. |
Rochelle Choenni; Ekaterina Shutova; Robert van Rooij; | |
| 112 | ConSeC: Word Sense Disambiguation As Continuous Sense Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation and drop this assumption, we propose CONtinuous SEnse Comprehension (ConSeC), a novel approach to WSD: leveraging a recent re-framing of this task as a text extraction problem, we adapt it to our formulation and introduce a feedback loop strategy that allows the disambiguation of a target word to be conditioned not only on its context but also on the explicit senses assigned to nearby words. |
Edoardo Barba; Luigi Procopio; Roberto Navigli; | code |
| 113 | Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we seek to further pursue this analysis into the realm of commonsense related language processing tasks. |
Ruben Branco; Ant?nio Branco; Jo?o Ant?nio Rodrigues; Jo?o Ricardo Silva; | |
| 114 | When Differential Privacy Meets NLP: The Devil Is in The Detail Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our contribution is a formal analysis of ADePT, a differentially private auto-encoder for text rewriting (Krishna et al, 2021). |
Ivan Habernal; | |
| 115 | Achieving Model Robustness Through Discrete Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this gap and leverage discrete attacks for online augmentation, where adversarial examples are generated at every training step, adapting to the changing nature of the model. |
Maor Ivgi; Jonathan Berant; | |
| 116 | Debiasing Methods in Natural Language Understanding Make Bias More Accessible Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a general probing-based framework that allows for post-hoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model’s representations. |
Michael Mendelson; Yonatan Belinkov; | |
| 117 | Evaluating The Robustness of Neural Language Models to Input Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we design and implement various types of character-level and word-level perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
Milad Moradi; Matthias Samwald; | |
| 118 | How Much Pretraining Data Do Language Models Need to Learn Syntax? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This calls for a study of the impact of pretraining data size on the knowledge of the models. We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data. |
Laura P?rez-Mayos; Miguel Ballesteros; Leo Wanner; | |
| 119 | Sorting Through The Noise: Testing Robustness of Information Processing in Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we tackle a component of this question by examining robustness of models’ ability to deploy relevant context information in the face of distracting content. |
Lalchand Pandia; Allyson Ettinger; | |
| 120 | Contrastive Explanations for Model Interpretability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method to produce contrastive explanations in the latent space, via a projection of the input representation, such that only the features that differentiate two potential decisions are captured. |
Alon Jacovi; Swabha Swayamdipta; Shauli Ravfogel; Yanai Elazar; Yejin Choi; Yoav Goldberg; | |
| 121 | On The Transferability of Adversarial Attacks Against Neural Text Classifier Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. |
Liping Yuan; Xiaoqing Zheng; Yi Zhou; Cho-Jui Hsieh; Kai-Wei Chang; | |
| 122 | Conditional Probing: Measuring Usable Information Beyond A Baseline Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend a theory of usable information called V-information and propose conditional probing, which explicitly conditions on the information in the baseline. |
John Hewitt; Kawin Ethayarajh; Percy Liang; Christopher Manning; | |
| 123 | GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs. |
Prafulla Kumar Choubey; Anna Currey; Prashant Mathur; Georgiana Dinu; | |
| 124 | Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. |
Mohammad Sadegh Rasooli; Chris Callison-Burch; Derry Tanti Wijaya; | |
| 125 | MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). |
Zewen Chi; Li Dong; Shuming Ma; Shaohan Huang; Saksham Singhal; Xian-Ling Mao; Heyan Huang; Xia Song; Furu Wei; | |
| 126 | Improving Zero-Shot Cross-Lingual Transfer Learning Via Robust Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. |
Kuan-Hao Huang; Wasi Ahmad; Nanyun Peng; Kai-Wei Chang; | |
| 127 | Speechformer: Reducing Information Loss in Direct Speech Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this issue, we propose Speechformer, an architecture that, thanks to reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. |
Sara Papi; Marco Gaido; Matteo Negri; Marco Turchi; | |
| 128 | Is moby Dick A Whale or A Bird? Named Entities and Terminology in Speech Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology. |
Marco Gaido; Susana Rodr?guez; Matteo Negri; Luisa Bentivogli; Marco Turchi; | |
| 129 | HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve effectiveness of the available BT data, we introduce HintedBT-a family of techniques which provides hints (through tags) to the encoder and decoder. |
Sahana Ramnath; Melvin Johnson; Abhirut Gupta; Aravindan Raghuveer; | |
| 130 | Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. |
Ashkan Alinejad; Hassan S. Shavarani; Anoop Sarkar; | |
| 131 | Nearest Neighbour Few-Shot Learning for Cross-lingual Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate cross-lingual adaptation using a simple nearest-neighbor few-shot (<15 samples) inference technique for classification tasks. |
M Saiful Bari; Batool Haider; Saab Mansour; | |
| 132 | Cross-Attention Is All You Need: Adapting Pretrained Transformers for Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. |
Mozhdeh Gheini; Xiang Ren; Jonathan May; | |
| 133 | Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand this bias, we study the tendency for transformer parameters to grow in magnitude (l2 norm) during training, and its implications for the emergent representations within self attention layers. |
William Merrill; Vivek Ramanujan; Yoav Goldberg; Roy Schwartz; Noah A. Smith; | |
| 134 | Foreseeing The Benefits of Incidental Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. |
Hangfeng He; Mingyuan Zhang; Qiang Ning; Dan Roth; | |
| 135 | Competency Problems: On Finding and Removing Artifacts in Language Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. |
Matt Gardner; William Merrill; Jesse Dodge; Matthew Peters; Alexis Ross; Sameer Singh; Noah A. Smith; | |
| 136 | Knowledge-Aware Meta-learning for Low-Resource Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. |
Huaxiu Yao; Ying-xin Wu; Maruan Al-Shedivat; Eric Xing; | |
| 137 | Sentence Bottleneck Autoencoders from Transformer Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. |
Ivan Montero; Nikolaos Pappas; Noah A. Smith; | |
| 138 | Efficient Contrastive Learning Via Novel Data Augmentation and Curriculum Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. |
Seonghyeon Ye; Jiseon Kim; Alice Oh; | |
| 139 | CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. |
Wenchang Ma; Ryuichi Takanobu; Minlie Huang; | |
| 140 | DIALKI: Knowledge Identification in Conversational Systems Through Dialogue-Document Contextualization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
Zeqiu Wu; Bo-Ru Lu; Hannaneh Hajishirzi; Mari Ostendorf; | |
| 141 | Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose models to play Iconary and train them on over 55,000 games between human players. |
Christopher Clark; Jordi Salvador; Dustin Schwenk; Derrick Bonafilia; Mark Yatskar; Eric Kolve; Alvaro Herrasti; Jonghyun Choi; Sachin Mehta; Sam Skjonsberg; Carissa Schoenick; Aaron Sarnat; Hannaneh Hajishirzi; Aniruddha Kembhavi; Oren Etzioni; Ali Farhadi; | |
| 142 | Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
Fei Mi; Wanhao Zhou; Lingjing Kong; Fengyu Cai; Minlie Huang; Boi Faltings; | |
| 143 | Contextual Rephrase Detection for Reducing Friction in Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. |
Zhuoyi Wang; Saurabh Gupta; Jie Hao; Xing Fan; Dingcheng Li; Alexander Hanbo Li; Chenlei Guo; | |
| 144 | Few-Shot Intent Detection Via Contrastive Pre-Training and Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. |
Jianguo Zhang; Trung Bui; Seunghyun Yoon; Xiang Chen; Zhiwei Liu; Congying Xia; Quan Hung Tran; Walter Chang; Philip Yu; | |
| 145 | It Doesn’t Look Good for A Date: Transforming Critiques Into Preferences for Conversational Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a method for transforming a user critique into a positive preference (e.g., I prefer more romantic) in order to retrieve reviews pertaining to potentially better recommendations (e.g., Perfect for a romantic dinner). |
Victor Bursztyn; Jennifer Healey; Nedim Lipka; Eunyee Koh; Doug Downey; Larry Birnbaum; | |
| 146 | AttentionRank: Unsupervised Keyphrase Extraction Using Self and Cross Attentions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. |
Haoran Ding; Xiao Luo; | |
| 147 | Unsupervised Relation Extraction: A Variational Autoencoder Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. |
Chenhan Yuan; Hoda Eldardiry; | |
| 148 | Robust Retrieval Augmented Generation for Zero-shot Slot Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
Michael Glass; Gaetano Rossiello; Md Faisal Mahbub Chowdhury; Alfio Gliozzo; | |
| 149 | Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While the advance of pretrained multilingual encoders suggests an easy optimism of train on English, run on any language, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. |
Mahsa Yarmohammadi; Shijie Wu; Marc Marone; Haoran Xu; Seth Ebner; Guanghui Qin; Yunmo Chen; Jialiang Guo; Craig Harman; Kenton Murray; Aaron Steven White; Mark Dredze; Benjamin Van Durme; | |
| 150 | Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. |
Sunipa Dev; Masoud Monajatipoor; Anaelia Ovalle; Arjun Subramonian; Jeff Phillips; Kai-Wei Chang; | |
| 151 | Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a post-processing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. |
Jialu Wang; Yang Liu; Xin Wang; | |
| 152 | Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer, by automatically re-writing the text itself. |
Fatemehsadat Mireshghallah; Taylor Berg-Kirkpatrick; | |
| 153 | Modeling Disclosive Transparency in NLP Application Descriptions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. |
Michael Saxon; Sharon Levy; Xinyi Wang; Alon Albalak; William Yang Wang; | |
| 154 | Reconstruction Attack on Instance Encoding for Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding. |
Shangyu Xie; Yuan Hong; | |
| 155 | Fairness-aware Class Imbalanced Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. |
Shivashankar Subramanian; Afshin Rahimi; Timothy Baldwin; Trevor Cohn; Lea Frermann; | |
| 156 | CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, , for low-latency secure inferences. |
Bo Feng; Qian Lou; Lei Jiang; Geoffrey Fox; | |
| 157 | Local Word Discovery for Interactive Transcription Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we propose a new computational task which is tuned to the available knowledge and interests in an Indigenous community, and which supports the construction of high quality texts and lexicons. |
William Lane; Steven Bird; | |
| 158 | Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. |
Mieradilijiang Maimaiti; Yang Liu; Yuanhang Zheng; Gang Chen; Kaiyu Huang; Ji Zhang; Huanbo Luan; Maosong Sun; | |
| 159 | Minimal Supervision for Morphological Inflection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small as five labeled inflection tables, accompanied by a large bulk of unlabeled text. |
Omer Goldman; Reut Tsarfaty; | |
| 160 | Fast WordPiece Tokenization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose efficient algorithms for the WordPiece tokenization used in BERT, from single-word tokenization to general text (e.g., sentence) tokenization. |
Xinying Song; Alex Salcianu; Yang Song; Dave Dopson; Denny Zhou; | |
| 161 | You Should Evaluate Your Language Model on Marginal Likelihood Over Tokenisations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that instead, language models should be evaluated on their marginal likelihood over tokenisations. |
Kris Cao; Laura Rimell; | |
| 162 | Broaden The Vision: Geo-Diverse Visual Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense. |
Da Yin; Liunian Harold Li; Ziniu Hu; Nanyun Peng; Kai-Wei Chang; | code |
| 163 | Reference-Centric Models for Grounded Collaborative Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game. |
Daniel Fried; Justin Chiu; Dan Klein; | |
| 164 | CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse question-answer pairs with the desired dataset style. |
Arjun Akula; Soravit Changpinyo; Boqing Gong; Piyush Sharma; Song-Chun Zhu; Radu Soricut; | |
| 165 | Visual Goal-Step Inference Using WikiHow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. |
Yue Yang; Artemis Panagopoulou; Qing Lyu; Li Zhang; Mark Yatskar; Chris Callison-Burch; | |
| 166 | Systematic Generalization on GSCAN: What Is Nearly Solved and What Is Next? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that a general-purpose Transformer-based model with cross-modal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work. Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context. Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment. |
Linlu Qiu; Hexiang Hu; Bowen Zhang; Peter Shaw; Fei Sha; | |
| 167 | Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To see how well this is achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). |
Taichi Iki; Akiko Aizawa; | |
| 168 | Neural Path Hunter: Reducing Hallucination in Dialogue Systems Via Path Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the task of improving faithfulness and reducing hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph (KG). |
Nouha Dziri; Andrea Madotto; Osmar Za?ane; Avishek Joey Bose; | code |
| 169 | Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate a controllable and coherent dialogue, in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for open-domain dialogue generation. |
Yicheng Zou; Zhihua Liu; Xingwu Hu; Qi Zhang; | |
| 170 | Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. |
Hyunwoo Kim; Byeongchang Kim; Gunhee Kim; | |
| 171 | Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem, we explore the hierarchical semantic of ontology and enhance the interrelation between slots with masked hierarchical attention. |
Xinmeng Li; Qian Li; Wansen Wu; Quanjun Yin; | |
| 172 | CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, in order to improve the diversity of both knowledge selection and knowledge-aware response generation, we propose a collaborative latent variable (CoLV) model to integrate these two aspects simultaneously in separate yet collaborative latent spaces, so as to capture the inherent correlation between knowledge selection and response generation. |
Haolan Zhan; Lei Shen; Hongshen Chen; Hainan Zhang; | |
| 173 | A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. |
Shilei Liu; Xiaofeng Zhao; Bochao Li; Feiliang Ren; Longhui Zhang; Shujuan Yin; | |
| 174 | Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a novel intention mechanism to better model deterministic entity knowledge. |
Zhiyuan Ma; Jianjun Li; Zezheng Zhang; Guohui Li; Yongjing Cheng; | |
| 175 | More Is Better: Enhancing Open-Domain Dialogue Generation Via Multi-Source Heterogeneous Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time. |
Sixing Wu; Ying Li; Minghui Wang; Dawei Zhang; Yang Zhou; Zhonghai Wu; | |
| 176 | Domain-Lifelong Learning for Dialogue State Tracking Via Knowledge Preservation Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. |
Qingbin Liu; Pengfei Cao; Cao Liu; Jiansong Chen; Xunliang Cai; Fan Yang; Shizhu He; Kang Liu; Jun Zhao; | |
| 177 | CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. |
Han Wu; Kun Xu; Linqi Song; | |
| 178 | Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. |
Yao Qiu; Jinchao Zhang; Jie Zhou; | |
| 179 | Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context. |
Leyang Cui; Yu Wu; Shujie Liu; Yue Zhang; | |
| 180 | An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. |
Kijong Han; Seojin Lee; Dong-hun Lee; | |
| 181 | Unsupervised Conversation Disentanglement Through Co-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore training a conversation disentanglement model without referencing any human annotations. |
Hui Liu; Zhan Shi; Xiaodan Zhu; | |
| 182 | Don’t Be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that consistency problem is more urgent in task-oriented domain. |
Libo Qin; Tianbao Xie; Shijue Huang; Qiguang Chen; Xiao Xu; Wanxiang Che; | |
| 183 | Transferable Persona-Grounded Dialogues Via Grounded Minimal Edits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the challenges, we propose the grounded minimal editing framework, which minimally edits existing responses to be grounded on the given concept. |
Chen Henry Wu; Yinhe Zheng; Xiaoxi Mao; Minlie Huang; | |
| 184 | EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. |
Hao Zhou; Minlie Huang; Yong Liu; Wei Chen; Xiaoyan Zhu; | |
| 185 | DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue. |
Che Liu; Rui Wang; Jinghua Liu; Jian Sun; Fei Huang; Luo Si; | |
| 186 | Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al. 2019, 2021). |
Shaopeng Lai; Ante Wang; Fandong Meng; Jie Zhou; Yubin Ge; Jiali Zeng; Junfeng Yao; Degen Huang; Jinsong Su; | code |
| 187 | Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a joint model, CG-T5, to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously. |
Feng Jiang; Yaxin Fan; Xiaomin Chu; Peifeng Li; Qiaoming Zhu; | |
| 188 | A Language Model-based Generative Classifier for Sentence-level Discourse Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problem, we propose a language model-based generative classifier (LMGC) for using more information from labels by treating the labels as an input while enhancing label representations by embedding descriptions for each label. |
Ying Zhang; Hidetaka Kamigaito; Manabu Okumura; | |
| 189 | Multimodal Phased Transformer for Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. |
Junyan Cheng; Iordanis Fostiropoulos; Barry Boehm; Mohammad Soleymani; | |
| 190 | Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. |
Linli Xu; Sijie Teng; Ruoyu Zhao; Junliang Guo; Chi Xiao; Deqiang Jiang; Bo Ren; | |
| 191 | RankNAS: Efficient Neural Architecture Search By Pairwise Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. |
Chi Hu; Chenglong Wang; Xiangnan Ma; Xia Meng; Yinqiao Li; Tong Xiao; Jingbo Zhu; Changliang Li; | |
| 192 | FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a new SSL framework called FLiText, which stands for Faster and Lighter semi-supervised Text classification. |
Chen Liu; Zhang Mengchao; Fu Zhibing; Panpan Hou; Yu Li; | |
| 193 | Evaluating Debiasing Techniques for Intersectional Biases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we argue that a truly fair model must consider ‘gerrymandering’ groups which comprise not only single attributes, but also intersectional groups. |
Shivashankar Subramanian; Xudong Han; Timothy Baldwin; Trevor Cohn; Lea Frermann; | |
| 194 | Definition Modelling for Appropriate Specificity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Herein, we propose a method for definition generation with appropriate specificity. |
Han Huang; Tomoyuki Kajiwara; Yuki Arase; | |
| 195 | Transductive Learning for Unsupervised Text Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel transductive learning approach in this paper, based on a retrieval-based context-aware style representation. |
Fei Xiao; Liang Pang; Yanyan Lan; Yan Wang; Huawei Shen; Xueqi Cheng; | |
| 196 | Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. |
Yong Guan; Shaoru Guo; Ru Li; Xiaoli Li; Hu Zhang; | |
| 197 | Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this case, we propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. |
Xin Tan; Longyin Zhang; Guodong Zhou; | |
| 198 | Adaptive Bridge Between Training and Inference for Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adaptive switching mechanism, which learns to automatically transit between ground-truth learning and generated learning regarding the word-level matching score, such as the cosine similarity. |
Haoran Xu; Hainan Zhang; Yanyan Zou; Hongshen Chen; Zhuoye Ding; Yanyan Lan; | |
| 199 | ConRPG: Paraphrase Generation Using Contexts As Regularizer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new unsupervised paradigm for paraphrase generation based on the assumption that the probabilities of generating two sentences with the same meaning given the same context should be the same. |
Yuxian Meng; Xiang Ao; Qing He; Xiaofei Sun; Qinghong Han; Fei Wu; Chun Fan; Jiwei Li; | |
| 200 | Building The Directed Semantic Graph for Coherent Long Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address aforementioned research issue, this paper proposes a novel two-stage approach to generate coherent long text. |
Ziao Wang; Xiaofeng Zhang; Hongwei Du; | |
| 201 | Iterative GNN-based Decoder for Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. |
Zichu Fei; Qi Zhang; Yaqian Zhou; | |
| 202 | Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. |
Fanyi Qu; Xin Jia; Yunfang Wu; | |
| 203 | Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraphrase generation with nonparallel data. |
Erguang Yang; Mingtong Liu; Deyi Xiong; Yujie Zhang; Yao Meng; Changjian Hu; Jinan Xu; Yufeng Chen; | |
| 204 | Exploring Task Difficulty for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. |
Jiale Han; Bo Cheng; Wei Lu; | |
| 205 | MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. |
Xinyin Ma; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Weiming Lu; | |
| 206 | Treasures Outside Contexts: Improving Event Detection Via Global Statistics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. |
Rui Li; Wenlin Zhao; Cheng Yang; Sen Su; | |
| 207 | Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. |
Pengfei Cao; Yubo Chen; Yuqing Yang; Kang Liu; Jun Zhao; | |
| 208 | A Novel Global Feature-Oriented Relational Triple Extraction Model Based on Table Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. |
Feiliang Ren; Longhui Zhang; Shujuan Yin; Xiaofeng Zhao; Shilei Liu; Bochao Li; Yaduo Liu; | code |
| 209 | Structure-Augmented Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. |
Jihyuk Kim; Myeongho Jeong; Seungtaek Choi; Seung-won Hwang; | |
| 210 | An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources. |
Yi Chen; Haiyun Jiang; Lemao Liu; Shuming Shi; Chuang Fan; Min Yang; Ruifeng Xu; | |
| 211 | DyLex: Incorporating Dynamic Lexicons Into BERT for Sequence Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DyLex, a plug-in lexicon incorporation approach for BERT based sequence labeling tasks. |
Baojun Wang; Zhao Zhang; Kun Xu; Guang-Yuan Hao; Yuyang Zhang; Lifeng Shang; Linlin Li; Xiao Chen; Xin Jiang; Qun Liu; | |
| 212 | MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. |
Manqing Dong; Chunguang Pan; Zhipeng Luo; | |
| 213 | Heterogeneous Graph Neural Networks for Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references. |
Jiacheng Ye; Ruijian Cai; Tao Gui; Qi Zhang; | |
| 214 | Machine Reading Comprehension As Data Augmentation: A Case Study on Implicit Event Argument Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). |
Jian Liu; Yufeng Chen; Jinan Xu; | |
| 215 | Importance Estimation from Multiple Perspectives for Keyphrase Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called as KIEMP) and further improve the performance of keyphrase extraction. |
Mingyang Song; Liping Jing; Lin Xiao; | |
| 216 | Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. |
Xuming Hu; Chenwei Zhang; Yawen Yang; Xiaohe Li; Li Lin; Lijie Wen; Philip S. Yu; | |
| 217 | Low-resource Taxonomy Enrichment with Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the problem of low-resource taxonomy enrichment, we propose Musubu, an efficient framework for taxonomy enrichment in low-resource settings with pretrained language models (LMs) as knowledge bases to compensate for the shortage of information. |
Kunihiro Takeoka; Kosuke Akimoto; Masafumi Oyamada; | |
| 218 | Entity Relation Extraction As Dependency Parsing in Visually Rich Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. |
Yue Zhang; Zhang Bo; Rui Wang; Junjie Cao; Chen Li; Zuyi Bao; | |
| 219 | Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types. |
Hui Wu; Xiaodong Shi; | |
| 220 | Less Is More: Pretrain A Strong Siamese Encoder for Dense Text Retrieval Using A Weak Decoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a new self-learning method that pre-trains the autoencoder using a weak decoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations. |
Shuqi Lu; Di He; Chenyan Xiong; Guolin Ke; Waleed Malik; Zhicheng Dou; Paul Bennett; Tie-Yan Liu; Arnold Overwijk; | code |
| 221 | TransPrompt: Towards An Automatic Transferable Prompting Framework for Few-shot Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on continuous prompt embeddings, we propose TransPrompt, a transferable prompting framework for few-shot learning across similar tasks. |
Chengyu Wang; Jianing Wang; Minghui Qiu; Jun Huang; Ming Gao; | |
| 222 | Weakly-supervised Text Classification Based on Keyword Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN. |
Lu Zhang; Jiandong Ding; Yi Xu; Yingyao Liu; Shuigeng Zhou; | |
| 223 | Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. |
Jingwei Yi; Fangzhao Wu; Chuhan Wu; Ruixuan Liu; Guangzhong Sun; Xing Xie; | |
| 224 | RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel joint training approach for dense passage retrieval and passage reranking. |
Ruiyang Ren; Yingqi Qu; Jing Liu; Wayne Xin Zhao; QiaoQiao She; Hua Wu; Haifeng Wang; Ji-Rong Wen; | code |
| 225 | Dealing with Typos for BERT-based Passage Retrieval and Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. |
Shengyao Zhuang; Guido Zuccon; | |
| 226 | From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. |
Xin Mao; Wenting Wang; Yuanbin Wu; Man Lan; | |
| 227 | Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze the redundancy present in encoded dense vectors and show that the default dimension of 768 is unnecessarily large. |
Xueguang Ma; Minghan Li; Kai Sun; Ji Xin; Jimmy Lin; | code |
| 228 | Relation Extraction with Word Graphs from N-grams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. |
Han Qin; Yuanhe Tian; Yan Song; | |
| 229 | A Bayesian Framework for Information-Theoretic Probing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents-allowing for more intuitive findings in scenarios with finite data. |
Tiago Pimentel; Ryan Cotterell; | |
| 230 | Masked Language Modeling and The Distributional Hypothesis: Order Word Matters Pre-training for Little Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. |
Koustuv Sinha; Robin Jia; Dieuwke Hupkes; Joelle Pineau; Adina Williams; Douwe Kiela; | |
| 231 | What’s Hidden in A One-layer Randomly Weighted Transformer? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that, hidden within one-layer randomly weighted neural networks, there exist subnetworks that can achieve impressive performance, without ever modifying the weight initializations, on machine translation tasks. |
Sheng Shen; Zhewei Yao; Douwe Kiela; Kurt Keutzer; Michael Mahoney; | |
| 232 | Rethinking Denoised Auto-Encoding in Language Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, such pre-training approaches are prone to learning representations that are covariant with the noise, leading to the discrepancy between the pre-training and fine-tuning stage. To remedy this, we present ContrAstive Pre-Training (CAPT) to learn noise invariant sequence representations. |
Fuli Luo; Pengcheng Yang; Shicheng Li; Xuancheng Ren; Xu Sun; Songfang Huang; Fei Huang; | |
| 233 | Lifelong Explainer for Lifelong Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Lifelong Explanation (LLE) approach that continuously trains a student explainer under the supervision of a teacher – an arbitrary explanation algorithm – on different tasks undertaken in LL. |
Xuelin Situ; Sameen Maruf; Ingrid Zukerman; Cecile Paris; Gholamreza Haffari; | code |
| 234 | Linguistic Dependencies and Statistical Dependence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we contribute an extensive analysis of the relationship between linguistic dependencies and statistical dependence between words. |
Jacob Louis Hoover; Wenyu Du; Alessandro Sordoni; Timothy J. O?Donnell; | |
| 235 | Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. |
Ryo Yoshida; Hiroshi Noji; Yohei Oseki; | |
| 236 | A Simple and Effective Positional Encoding for Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this, we introduce Decoupled Positional Attention for Transformers (DIET), a simple yet effective mechanism to encode position and segment information into the Transformer models. |
Pu-Chin Chen; Henry Tsai; Srinadh Bhojanapalli; Hyung Won Chung; Yin-Wen Chang; Chun-Sung Ferng; | |
| 237 | Explore Better Relative Position Embeddings from Encoding Perspective for Transformer Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the potential problems in Shaw-RPE and XL-RPE, which are the most representative and prevalent RPEs, and propose two novel RPEs called Low-level Fine-grained High-level Coarse-grained (LFHC) RPE and Gaussian Cumulative Distribution Function (GCDF) RPE. |
Anlin Qu; Jianwei Niu; Shasha Mo; | |
| 238 | Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose the Adversarial Mixing Policy (AMP), organized in a min-max-rand formulation, to relax the Locally Linear Constraints in Mixup. |
Guang Liu; Yuzhao Mao; Huang Hailong; Gao Weiguo; Li Xuan; | |
| 239 | Is This The End of The Gold Standard? A Straightforward Reference-less Grammatical Error Correction Metric Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. |
Md Asadul Islam; Enrico Magnani; | |
| 240 | Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. |
Vladimir Araujo; Andr?s Villa; Marcelo Mendoza; Marie-Francine Moens; Alvaro Soto; | |
| 241 | Backdoor Attacks on Pre-trained Models By Layerwise Weight Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a stronger weight-poisoning attack method that introduces a layerwise weight poisoning strategy to plant deeper backdoors; we also introduce a combinatorial trigger that cannot be easily detected. |
Linyang Li; Demin Song; Xiaonan Li; Jiehang Zeng; Ruotian Ma; Xipeng Qiu; | |
| 242 | GAML-BERT: Improving BERT Early Exiting By Gradient Aligned Mutual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel framework, Gradient Aligned Mutual Learning BERT (GAML-BERT), for improving the early exiting of BERT. |
Wei Zhu; Xiaoling Wang; Yuan Ni; Guotong Xie; | |
| 243 | The Power of Scale for Parameter-Efficient Prompt Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore prompt tuning, a simple yet effective mechanism for learning soft prompts to condition frozen language models to perform specific downstream tasks. |
Brian Lester; Rami Al-Rfou; Noah Constant; | |
| 244 | Scalable Font Reconstruction with Dual Latent Manifolds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. |
Nikita Srivatsan; Si Wu; Jonathan Barron; Taylor Berg-Kirkpatrick; | |
| 245 | Neuro-Symbolic Approaches for Text-Based Policy Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present SymboLic Action policy for Textual Environments (SLATE), that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. |
Subhajit Chaudhury; Prithviraj Sen; Masaki Ono; Daiki Kimura; Michiaki Tatsubori; Asim Munawar; | |
| 246 | Layer-wise Model Pruning Based on Mutual Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to preserved neurons in the upper layer are preserved. |
Chun Fan; Jiwei Li; Tianwei Zhang; Xiang Ao; Fei Wu; Yuxian Meng; Xiaofei Sun; | |
| 247 | Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method called SHINE, which is based on graph neural network (GNN), for short text classification. |
Yaqing Wang; Song Wang; Quanming Yao; Dejing Dou; | |
| 248 | KFolden: K-Fold Ensemble for Out-Of-Distribution Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a simple yet effective framework kFolden, which mimics the behaviors of OOD detection during training without the use of any external data. |
Xiaoya Li; Jiwei Li; Xiaofei Sun; Chun Fan; Tianwei Zhang; Fei Wu; Yuxian Meng; Jun Zhang; | |
| 249 | Frustratingly Simple Pretraining Alternatives to Masked Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore five simple pretraining objectives based on token-level classification tasks as replacements of MLM. |
Atsuki Yamaguchi; George Chrysostomou; Katerina Margatina; Nikolaos Aletras; | |
| 250 | HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information. |
Chenhe Dong; Yaliang Li; Ying Shen; Minghui Qiu; | code |
| 251 | Searching for An Effective Defender: Benchmarking Defense Against Adversarial Word Substitution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. |
Zongyi Li; Jianhan Xu; Jiehang Zeng; Linyang Li; Xiaoqing Zheng; Qi Zhang; Kai-Wei Chang; Cho-Jui Hsieh; | code |
| 252 | Re-embedding Difficult Samples Via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Mutual Information constrained Semantically Oversampling framework (MISO) that can generate anchor instances to help the backbone network determine the re-embedding position of a non-overlapping representation for each difficult sample. |
Jiachen Tian; Shizhan Chen; Xiaowang Zhang; Zhiyong Feng; Deyi Xiong; Shaojuan Wu; Chunliu Dou; | |
| 253 | Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification Using Heterogeneous Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we propose a novel neural network based approach for multi-label document classification, in which two heterogeneous graphs are constructed and learned using heterogeneous graph transformers. |
Chenchen Ye; Linhai Zhang; Yulan He; Deyu Zhou; Jie Wu; | |
| 254 | Natural Language Processing Meets Quantum Physics: A Survey and Categorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. |
Sixuan Wu; Jian Li; Peng Zhang; Yue Zhang; | |
| 255 | MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. |
Zheng Li; Danqing Zhang; Tianyu Cao; Ying Wei; Yiwei Song; Bing Yin; | |
| 256 | Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into an NMT model to improve translation performance. |
Weixuan Wang; Wei Peng; Meng Zhang; Qun Liu; | |
| 257 | Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose an algorithm VoCap to determine the desired vocabulary capacity of each language. |
Bo Zheng; Li Dong; Shaohan Huang; Saksham Singhal; Wanxiang Che; Ting Liu; Xia Song; Furu Wei; | code |
| 258 | Recurrent Attention for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN) . |
Jiali Zeng; Shuangzhi Wu; Yongjing Yin; Yufan Jiang; Mu Li; | |
| 259 | Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we focus on mitigating noise in augmented data. |
Yingmei Guo; Linjun Shou; Jian Pei; Ming Gong; Mingxing Xu; Zhiyong Wu; Daxin Jiang; | |
| 260 | Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a redundant head enlivening (RHE) method to precisely identify redundant heads, and then vitalize their potential by learning syntactic relations and prior knowledge in the text without sacrificing the roles of important heads. |
Tianfu Zhang; Heyan Huang; Chong Feng; Longbing Cao; | |
| 261 | Unsupervised Neural Machine Translation with Universal Grammar Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we seek to leverage such shared grammar clues to provide more explicit language parallel signals to enhance the training of unsupervised machine translation models. |
Zuchao Li; Masao Utiyama; Eiichiro Sumita; Hai Zhao; | |
| 262 | Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we apply one translation per discourse in NMT, and aim to encourage lexical translation consistency for document-level NMT. |
Xinglin Lyu; Junhui Li; Zhengxian Gong; Min Zhang; | |
| 263 | Improving Neural Machine Translation By Bidirectional Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple and effective pretraining strategy – bidirectional training (BiT) for neural machine translation. |
Liang Ding; Di Wu; Dacheng Tao; | |
| 264 | Scheduled Sampling Based on Decoding Steps for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the above discrepancy, we propose scheduled sampling methods based on decoding steps, increasing the selection chance of predicted tokens with the growth of decoding steps. |
Yijin Liu; Fandong Meng; Yufeng Chen; Jinan Xu; Jie Zhou; | |
| 265 | Learning to Rewrite for Non-Autoregressive Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an architecture named RewriteNAT to explicitly learn to rewrite the erroneous translation pieces. |
Xinwei Geng; Xiaocheng Feng; Bing Qin; | |
| 266 | SHAPE : Shifted Absolute Position Embedding for Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. |
Shun Kiyono; Sosuke Kobayashi; Jun Suzuki; Kentaro Inui; | |
| 267 | Self-Supervised Quality Estimation for Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce the negative impact of noises, we propose a self-supervised method for both sentence- and word-level QE, which performs quality estimation by recovering the masked target words. |
Yuanhang Zheng; Zhixing Tan; Meng Zhang; Mieradilijiang Maimaiti; Huanbo Luan; Maosong Sun; Qun Liu; Yang Liu; | |
| 268 | Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. |
Thuy-Trang Vu; Xuanli He; Dinh Phung; Gholamreza Haffari; | |
| 269 | STANKER: Stacking Network Based on Level-grained Attention-masked BERT for Rumor Detection on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). |
Dongning Rao; Xin Miao; Zhihua Jiang; Ran Li; | |
| 270 | ActiveEA: Active Learning for Neural Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. |
Bing Liu; Harrisen Scells; Guido Zuccon; Wen Hua; Genghong Zhao; | |
| 271 | Cost-effective End-to-end Information Extraction for Semi-structured Document Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present our recent effort on transitioning from our existing pipeline-based IE system to an end-to-end system focusing on practical challenges that are associated with replacing and deploying the system in real, large-scale production. |
Wonseok Hwang; Hyunji Lee; Jinyeong Yim; Geewook Kim; Minjoon Seo; | |
| 272 | Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. |
Weijiang Yu; Yingpeng Wen; Fudan Zheng; Nong Xiao; | |
| 273 | GraphMR: Graph Neural Network for Mathematical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers. |
Weijie Feng; Binbin Liu; Dongpeng Xu; Qilong Zheng; Yun Xu; | |
| 274 | What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. |
Boseop Kim; HyoungSeok Kim; Sang-Woo Lee; Gichang Lee; Donghyun Kwak; Jeon Dong Hyeon; Sunghyun Park; Sungju Kim; Seonhoon Kim; Dongpil Seo; Heungsub Lee; Minyoung Jeong; Sungjae Lee; Minsub Kim; Suk Hyun Ko; Seokhun Kim; Taeyong Park; Jinuk Kim; Soyoung Kang; Na-Hyeon Ryu; Kang Min Yoo; Minsuk Chang; Soobin Suh; Sookyo In; Jinseong Park; Kyungduk Kim; Hiun Kim; Jisu Jeong; Yong Goo Yeo; Donghoon Ham; Dongju Park; Min Young Lee; Jaewook Kang; Inho Kang; Jung-Woo Ha; Woomyoung Park; Nako Sung; | |
| 275 | APIRecX: Cross-Library API Recommendation Via Pre-Trained Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose APIRecX, the first cross-library API recommendation approach, which uses BPE to split each API call in each API sequence and pre-trains a GPT based language model. |
Yuning Kang; Zan Wang; Hongyu Zhang; Junjie Chen; Hanmo You; | |
| 276 | GMH: A General Multi-hop Reasoning Model for KG Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. |
Yao Zhang; Hongru Liang; Adam Jatowt; Wenqiang Lei; Xin Wei; Ning Jiang; Zhenglu Yang; | |
| 277 | BPM_MT: Enhanced Backchannel Prediction Model Using Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model. |
Jin Yea Jang; San Kim; Minyoung Jung; Saim Shin; Gahgene Gweon; | |
| 278 | Graphine: A Dataset for Graph-aware Terminology Definition Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. |
Zequn Liu; Shukai Wang; Yiyang Gu; Ruiyi Zhang; Ming Zhang; Sheng Wang; | |
| 279 | Leveraging Order-Free Tag Relations for Context-Aware Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. |
Junmo Kang; Jeonghwan Kim; Suwon Shin; Sung-Hyon Myaeng; | |
| 280 | End-to-End Conversational Search for Online Shopping with Utterance Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. |
Liqiang Xiao; Jun Ma; Xin Luna Dong; Pascual Mart?nez-G?mez; Nasser Zalmout; Wei Chen; Tong Zhao; Hao He; Yaohui Jin; | |
| 281 | Self-Supervised Curriculum Learning for Spelling Error Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study how to further improve the performance of the state-of-the-art SEC method with CL, and propose a Self-Supervised Curriculum Learning (SSCL) approach. |
Zifa Gan; Hongfei Xu; Hongying Zan; | |
| 282 | Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these, we propose an intuitive yet effective general framework (called Fix-Filter-Fix or F^3) for bug fixing. |
Haiwen Hong; Jingfeng Zhang; Yin Zhang; Yao Wan; Yulei Sui; | |
| 283 | Neuro-Symbolic Reinforcement Learning with First-Order Logic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. |
Daiki Kimura; Masaki Ono; Subhajit Chaudhury; Ryosuke Kohita; Akifumi Wachi; Don Joven Agravante; Michiaki Tatsubori; Asim Munawar; Alexander Gray; | |
| 284 | Biomedical Concept Normalization By Leveraging Hypernyms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we exploit biomedical concept hypernyms to facilitate BCN. |
Cheng Yan; Yuanzhe Zhang; Kang Liu; Jun Zhao; Yafei Shi; Shengping Liu; | |
| 285 | Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). |
Xin Liu; Qingcai Chen; Junying Chen; Wenxiu Zhou; Tingyu Liu; Xinlan Yang; Weihua Peng; | |
| 286 | Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a model augmented with hierarchical contextualized representation to extract more information from both sentence-level and discourse-level. |
Shuqun Li; Liang Yang; Weidong He; Shiqi Zhang; Jingjie Zeng; Hongfei Lin; | |
| 287 | SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, we propose SpellBERT, a pretrained model with graph-based extra features and independent on confusion set. |
Tuo Ji; Hang Yan; Xipeng Qiu; | |
| 288 | Automated Generation of Accurate & Fluent Medical X-ray Reports Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. |
Hoang Nguyen; Dong Nie; Taivanbat Badamdorj; Yujie Liu; Yingying Zhu; Jason Truong; Li Cheng; | |
| 289 | Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM). |
Xingwu Sun; Yanling Cui; Hongyin Tang; Fuzheng Zhang; Beihong Jin; Shi Wang; | |
| 290 | Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. |
Zhiwei Zhang; Jiyi Li; Fumiyo Fukumoto; Yanming Ye; | |
| 291 | A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we start from joint word segmentation and POS tagging, presenting a fine-grained domain adaption method to model the gaps accurately. |
Peijie Jiang; Dingkun Long; Yueheng Sun; Meishan Zhang; Guangwei Xu; Pengjun Xie; | |
| 292 | Answering Open-Domain Questions of Varying Reasoning Steps from Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We employ a single multi-task transformer model to perform all the necessary subtasks-retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents-in an iterative fashion. |
Peng Qi; Haejun Lee; Tg Sido; Christopher Manning; | code |
| 293 | Adaptive Information Seeking for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. |
Yunchang Zhu; Liang Pang; Yanyan Lan; Huawei Shen; Xueqi Cheng; | |
| 294 | Mapping Probability Word Problems to Executable Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we employ and analyse various neural models for answering such word problems. |
Simon Suster; Pieter Fivez; Pietro Totis; Angelika Kimmig; Jesse Davis; Luc de Raedt; Walter Daelemans; | |
| 295 | Enhancing Multiple-choice Machine Reading Comprehension By Punishing Illogical Interpretations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiple-choice MRC and improve the model by punishing the illogical attributions. |
Yiming Ju; Yuanzhe Zhang; Zhixing Tian; Kang Liu; Xiaohuan Cao; Wenting Zhao; Jinlong Li; Jun Zhao; | |
| 296 | Large-Scale Relation Learning for Question Answering Over Knowledge Bases with Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the gap between the natural language and the structured KB, we propose three relation learning tasks for BERT-based KBQA, including relation extraction, relation matching, and relation reasoning. |
Yuanmeng Yan; Rumei Li; Sirui Wang; Hongzhi Zhang; Zan Daoguang; Fuzheng Zhang; Wei Wu; Weiran Xu; | |
| 297 | Phrase Retrieval Learns Passage Retrieval, Too Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. |
Jinhyuk Lee; Alexander Wettig; Danqi Chen; | |
| 298 | Neural Natural Logic Inference for Interpretable Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models. |
Jihao Shi; Xiao Ding; Li Du; Ting Liu; Bing Qin; | |
| 299 | Smoothing Dialogue States for Open Conversational Machine Reading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference. |
Zhuosheng Zhang; Siru Ouyang; Hai Zhao; Masao Utiyama; Eiichiro Sumita; | |
| 300 | FinQA: A Dataset of Numerical Reasoning Over Financial Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. |
Zhiyu Chen; Wenhu Chen; Charese Smiley; Sameena Shah; Iana Borova; Dylan Langdon; Reema Moussa; Matt Beane; Ting-Hao Huang; Bryan Routledge; William Yang Wang; | code |
| 301 | FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. |
Kushal Lakhotia; Bhargavi Paranjape; Asish Ghoshal; Scott Yih; Yashar Mehdad; Srini Iyer; | |
| 302 | RockNER: A Simple Method to Create Adversarial Examples for Evaluating The Robustness of Named Entity Recognition Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. |
Bill Yuchen Lin; Wenyang Gao; Jun Yan; Ryan Moreno; Xiang Ren; | |
| 303 | Diagnosing The First-Order Logical Reasoning Ability Through LogicNLI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI. |
Jidong Tian; Yitian Li; Wenqing Chen; Liqiang Xiao; Hao He; Yaohui Jin; | |
| 304 | Constructing A Psychometric Testbed for Fair Natural Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we describe our efforts to construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. |
Ahmed Abbasi; David Dobolyi; John P. Lalor; Richard G. Netemeyer; Kendall Smith; Yi Yang; | |
| 305 | COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. |
Xinliang Frederick Zhang; Heming Sun; Xiang Yue; Simon Lin; Huan Sun; | code |
| 306 | Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a Chinese dataset for evaluating pretrained language models on Word Prediction given Long-term Context (Chinese WPLC). |
Huibin Ge; Chenxi Sun; Deyi Xiong; Qun Liu; | code |
| 307 | WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better evaluate NLMs, we propose a logic-based framework that focuses on high-quality commonsense knowledge. |
Weinan He; Canming Huang; Yongmei Liu; Xiaodan Zhu; | |
| 308 | Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The first is a new pretraining task that trains MLMs on anaphoric relations with explicit supervision, and the second proposal is a new finetuning method that remedies a notorious issue, the pretrain-finetune discrepancy. |
Ryuto Konno; Shun Kiyono; Yuichiroh Matsubayashi; Hiroki Ouchi; Kentaro Inui; | |
| 309 | Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. |
Liang Wang; Wei Zhao; Jingming Liu; | |
| 310 | Total Recall: A Customized Continual Learning Method for Neural Semantic Parsers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose TotalRecall, a continual learning method designed for neural semantic parsers from two aspects: i) a sampling method for memory replay that diversifies logical form templates and balances distributions of parse actions in a memory; ii) a two-stage training method that significantly improves generalization capability of the parsers across tasks. |
Zhuang Li; Lizhen Qu; Gholamreza Haffari; | |
| 311 | Exophoric Pronoun Resolution in Dialogues with Topic Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem. |
Xintong Yu; Hongming Zhang; Yangqiu Song; Changshui Zhang; Kun Xu; Dong Yu; | |
| 312 | Context-Aware Interaction Network for Question Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. |
Zhe Hu; Zuohui Fu; Yu Yin; Gerard de Melo; | |
| 313 | TEMP: Taxonomy Expansion with Dynamic Margin Loss Through Taxonomy-Paths Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. |
Zichen Liu; Hongyuan Xu; Yanlong Wen; Ning Jiang; HaiYing Wu; Xiaojie Yuan; | |
| 314 | A Graph-Based Neural Model for End-to-End Frame Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an end-to-end neural model to tackle the task jointly. |
ZhiChao Lin; Yueheng Sun; Meishan Zhang; | |
| 315 | Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. |
Kun Zhou; Wayne Xin Zhao; Sirui Wang; Fuzheng Zhang; Wei Wu; Ji-Rong Wen; | code |
| 316 | CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning. |
Zhenxi Lin; Qianli Ma; Jiangyue Yan; Jieyu Chen; | |
| 317 | To Be Closer: Learning to Link Up Aspects with Opinions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. |
Yuxiang Zhou; Lejian Liao; Yang Gao; Zhanming Jie; Wei Lu; | |
| 318 | Seeking Common But Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model. |
Hongjiang Jing; Zuchao Li; Hai Zhao; Shu Jiang; | |
| 319 | Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. |
Jianzhu Bao; Bin Liang; Jingyi Sun; Yice Zhang; Min Yang; Ruifeng Xu; | |
| 320 | Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. |
Dayu Li; Xiaodan Zhu; Yang Li; Suge Wang; Deyu Li; Jian Liao; Jianxing Zheng; | |
| 321 | Improving Federated Learning for Aspect-based Sentiment Analysis Via Topic Memories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. |
Han Qin; Guimin Chen; Yuanhe Tian; Yan Song; | |
| 322 | Comparative Opinion Quintuple Extraction from Product Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). |
Ziheng Liu; Rui Xia; Jianfei Yu; | |
| 323 | CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. |
Hang Li; Wenbiao Ding; Yu Kang; Tianqiao Liu; Zhongqin Wu; Zitao Liu; | code |
| 324 | Relation-aware Video Reading Comprehension for Temporal Language Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. |
Jialin Gao; Xin Sun; Mengmeng Xu; Xi Zhou; Bernard Ghanem; | code |
| 325 | Mutual-Learning Improves End-to-End Speech Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an alternative-a trainable mutual-learning scenario, where the MT and the ST models are collaboratively trained and are considered as peers, rather than teacher/student. |
Jiawei Zhao; Wei Luo; Boxing Chen; Andrew Gilman; | |
| 326 | Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simple yet effective method to construct vision guided (VG) GPLMs for the MAS task using attention-based add-on layers to incorporate visual information while maintaining their original text generation ability. |
Tiezheng Yu; Wenliang Dai; Zihan Liu; Pascale Fung; | |
| 327 | Natural Language Video Localization with Learnable Moment Proposals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the performance of propose-and-rank models are underestimated due to the predefined manners: 1) Hand-designed rules are hard to guarantee the complete coverage of targeted segments. |
Shaoning Xiao; Long Chen; Jian Shao; Yueting Zhuang; Jun Xiao; | |
| 328 | Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation. |
Sonia Raychaudhuri; Saim Wani; Shivansh Patel; Unnat Jain; Angel Chang; | |
| 329 | How to Leverage The Multimodal EHR Data for Better Medical Prediction? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. |
Bo Yang; Lijun Wu; | |
| 330 | Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document. |
Jingun Kwon; Naoki Kobayashi; Hidetaka Kamigaito; Manabu Okumura; | |
| 331 | Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. |
Yong Guan; Shaoru Guo; Ru Li; Xiaoli Li; Hongye Tan; | |
| 332 | CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs. |
Ensheng Shi; Yanlin Wang; Lun Du; Hongyu Zhang; Shi Han; Dongmei Zhang; Hongbin Sun; | code |
| 333 | SgSum:Transforming Multi-document Summarization Into Sub-graph Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. |
Moye Chen; Wei Li; Jiachen Liu; Xinyan Xiao; Hua Wu; Haifeng Wang; | |
| 334 | Event Graph Based Sentence Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the effective sentence fusion method in the context of text summarization. |
Ruifeng Yuan; Zili Wang; Wenjie Li; | |
| 335 | Transformer-based Lexically Constrained Headline Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformer-based method that guarantees to include the given phrase in the high-quality generated headline. |
Kosuke Yamada; Yuta Hitomi; Hideaki Tamori; Ryohei Sasano; Naoaki Okazaki; Kentaro Inui; Koichi Takeda; | |
| 336 | Learn to Copy from The Copying History: Correlational Copy Network for Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel copying scheme named Correlational Copying Network (CoCoNet) that enhances the standard copying mechanism by keeping track of the copying history. |
Haoran Li; Song Xu; Peng Yuan; Yujia Wang; Youzheng Wu; Xiaodong He; Bowen Zhou; | |
| 337 | Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed an efficient weak-supervised adversarial data augmentation approach to form the factual consistency dataset. |
Zhiyuan Zeng; Jiaze Chen; Weiran Xu; Lei Li; | |
| 338 | Word Reordering for Zero-shot Cross-lingual Structured Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. |
Tao Ji; Yong Jiang; Tao Wang; Zhongqiang Huang; Fei Huang; Yuanbin Wu; Xiaoling Wang; | |
| 339 | A Unified Encoding of Structures in Transition Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel attention-based encoder unifying representation of all structures in a transition system. |
Tao Ji; Yong Jiang; Tao Wang; Zhongqiang Huang; Fei Huang; Yuanbin Wu; Xiaoling Wang; | |
| 340 | Improving Unsupervised Question Answering Via Summarization-Informed Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to overcome these shortcomings, we propose a distantly-supervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. |
Chenyang Lyu; Lifeng Shang; Yvette Graham; Jennifer Foster; Xin Jiang; Qun Liu; | |
| 341 | TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering Over Relation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. |
Jiaxin Shi; Shulin Cao; Lei Hou; Juanzi Li; Hanwang Zhang; | |
| 342 | Topic Transferable Table Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTable-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTable-Questions datasets. |
Saneem Chemmengath; Vishwajeet Kumar; Samarth Bharadwaj; Jaydeep Sen; Mustafa Canim; Soumen Chakrabarti; Alfio Gliozzo; Karthik Sankaranarayanan; | |
| 343 | WebSRC: A Dataset for Web-Based Structural Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the task of web-based structural reading comprehension. |
Xingyu Chen; Zihan Zhao; Lu Chen; JiaBao Ji; Danyang Zhang; Ao Luo; Yuxuan Xiong; Kai Yu; | |
| 344 | Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. |
Avia Efrat; Uri Shaham; Dan Kilman; Omer Levy; | |
| 345 | End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. |
Amir Saffari; Armin Oliya; Priyanka Sen; Tom Ayoola; | |
| 346 | Improving Query Graph Generation for Complex Question Answering Over Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. |
Kechen Qin; Cheng Li; Virgil Pavlu; Javed Aslam; | |
| 347 | DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DiscoDVT, a discourse-aware discrete variational Transformer to tackle the incoherence issue. |
Haozhe Ji; Minlie Huang; | |
| 348 | Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations. |
Tianqiao Liu; Qiang Fang; Wenbiao Ding; Hang Li; Zhongqin Wu; Zitao Liu; | code |
| 349 | Generic Resources Are What You Need: Style Transfer Tasks Without Task-specific Parallel Training Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source-target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
Huiyuan Lai; Antonio Toral; Malvina Nissim; | |
| 350 | Revisiting Pivot-Based Paraphrase Generation: Language Is Not The Only Optional Pivot Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the feasibility of using semantic and syntactic representations as the pivot for paraphrase generation. |
Yitao Cai; Yue Cao; Xiaojun Wan; | |
| 351 | Structural Adapters in Pretrained Language Models for AMR-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. |
Leonardo F. R. Ribeiro; Yue Zhang; Iryna Gurevych; | |
| 352 | Data-to-text Generation By Splicing Together Nearest Neighbors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from neighbor source-target pairs. |
Sam Wiseman; Arturs Backurs; Karl Stratos; | |
| 353 | Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve this, we propose a COntext-aware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities. |
Yanjie Gou; Yinjie Lei; Lingqiao Liu; Yong Dai; Chunxu Shen; | |
| 354 | Efficient Dialogue Complementary Policy Learning Via Deep Q-network Policy and Episodic Memory Policy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the human brain, this paper proposes a novel complementary policy learning (CPL) framework, which exploits the complementary advantages of the episodic memory (EM) policy and the deep Q-network (DQN) policy to achieve fast and effective dialogue policy learning. |
Yangyang Zhao; Zhenyu Wang; Changxi Zhu; Shihan Wang; | |
| 355 | CRFR: Improving Conversational Recommender Systems Via Flexible Fragments Reasoning on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model. |
Jinfeng Zhou; Bo Wang; Ruifang He; Yuexian Hou; | |
| 356 | DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. |
Zeming Liu; Haifeng Wang; Zheng-Yu Niu; Hua Wu; Wanxiang Che; | |
| 357 | End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). |
Dinesh Raghu; Shantanu Agarwal; Sachindra Joshi; Mausam; | |
| 358 | Dimensional Emotion Detection from Categorical Emotion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. |
Sungjoon Park; Jiseon Kim; Seonghyeon Ye; Jaeyeol Jeon; Hee Young Park; Alice Oh; | |
| 359 | Not All Negatives Are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. |
Varsha Suresh; Desmond Ong; | |
| 360 | Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA). |
Xincheng Ju; Dong Zhang; Rong Xiao; Junhui Li; Shoushan Li; Min Zhang; Guodong Zhou; | |
| 361 | Solving Aspect Category Sentiment Analysis As A Text Generation Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. |
Jian Liu; Zhiyang Teng; Leyang Cui; Hanmeng Liu; Yue Zhang; | |
| 362 | Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. |
Ting-Wei Hsu; Chung-Chi Chen; Hen-Hsen Huang; Hsin-Hsi Chen; | |
| 363 | The Effect of Round-Trip Translation on Fairness in Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the impact of round-trip translation on the demographic parity of sentiment classifiers and show how round-trip translation consistently improves classification fairness at test time (reducing up to 47% of between-group gaps). |
Jonathan Christiansen; Mathias Gammelgaard; Anders S?gaard; | |
| 364 | CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose CHoRaL, a framework to generate perceived humor labels on Facebook posts, using the naturally available user reactions to these posts with no manual annotation needed. |
Zixiaofan Yang; Shayan Hooshmand; Julia Hirschberg; | |
| 365 | CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS). |
Haitao Lin; Liqun Ma; Junnan Zhu; Lu Xiang; Yu Zhou; Jiajun Zhang; Chengqing Zong; | |
| 366 | CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present the problem of cross-document RE, making an initial step towards knowledge acquisition in the wild. |
Yuan Yao; Jiaju Du; Yankai Lin; Peng Li; Zhiyuan Liu; Jie Zhou; Maosong Sun; | |
| 367 | Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. |
Mohammad Aliannejadi; Julia Kiseleva; Aleksandr Chuklin; Jeff Dalton; Mikhail Burtsev; | |
| 368 | We Need to Talk About Train-dev-test Splits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to use a tune-set when developing neural network methods, which can be used for model picking so that comparing the different versions of a new model can safely be done on the development data. |
Rob van der Goot; | |
| 369 | PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. |
Long Doan; Linh The Nguyen; Nguyen Luong Tran; Thai Hoang; Dat Quoc Nguyen; | code |
| 370 | Lying Through One’s Teeth: A Study on Verbal Leakage Cues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study verbal leakage cues to understand the effect of the data construction method on their significance, and examine the relationship between such cues and models’ validity. |
Min-Hsuan Yeh; Lun-Wei Ku; | |
| 371 | Multi-granularity Textual Adversarial Attack with Behavior Cloning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. |
Yangyi Chen; Jin Su; Wei Wei; | code |
| 372 | All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we call into question the informativity of such measures for contextualized language models. |
William Timkey; Marten van Schijndel; | |
| 373 | Incorporating Residual and Normalization Layers Into Analysis of Masked Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. |
Goro Kobayashi; Tatsuki Kuribayashi; Sho Yokoi; Kentaro Inui; | |
| 374 | Mind The Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. |
Fanchao Qi; Yangyi Chen; Xurui Zhang; Mukai Li; Zhiyuan Liu; Maosong Sun; | code |
| 375 | Sociolectal Analysis of Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes. |
Sheng Zhang; Xin Zhang; Weiming Zhang; Anders S?gaard; | |
| 376 | Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT’s contextual representations for nine diverse languages. |
Tomasz Limisiewicz; David Marecek; | |
| 377 | Are Transformers A Modern Version of ELIZA? Observations on French Object Verb Agreement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks’ syntactic ability. |
Bingzhi Li; Guillaume Wisniewski; Benoit Crabb?; | |
| 378 | Fine-grained Entity Typing Via Label Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. |
Qing Liu; Hongyu Lin; Xinyan Xiao; Xianpei Han; Le Sun; Hua Wu; | |
| 379 | Enhanced Language Representation with Label Knowledge for Span Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address those problems, we introduce a fresh paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. |
Pan Yang; Xin Cong; Zhenyu Sun; Xingwu Liu; | |
| 380 | PRIDE: Predicting Relationships in Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. |
Anna Tigunova; Paramita Mirza; Andrew Yates; Gerhard Weikum; | |
| 381 | Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. |
Ian Magnusson; Scott Friedman; | |
| 382 | Sequential Cross-Document Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. |
Emily Allaway; Shuai Wang; Miguel Ballesteros; | |
| 383 | Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs Into BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. |
Zaiqiao Meng; Fangyu Liu; Thomas Clark; Ehsan Shareghi; Nigel Collier; | |
| 384 | Filling The Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE – 100 CE). |
Koren Lazar; Benny Saret; Asaf Yehudai; Wayne Horowitz; Nathan Wasserman; Gabriel Stanovsky; | |
| 385 | AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain specific vocabulary based on a tokenization statistic. |
Jimin Hong; TaeHee Kim; Hyesu Lim; Jaegul Choo; | |
| 386 | Can We Improve Model Robustness Through Secondary Attribute Counterfactuals? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we study how and why modeling counterfactuals over multiple attributes can go significantly further in improving model performance. |
Ananth Balashankar; Xuezhi Wang; Ben Packer; Nithum Thain; Ed Chi; Alex Beutel; | |
| 387 | Long-Range Modeling of Source Code Files with EWASH: Extended Window Access By Syntax Hierarchy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window. |
Colin Clement; Shuai Lu; Xiaoyu Liu; Michele Tufano; Dawn Drain; Nan Duan; Neel Sundaresan; Alexey Svyatkovskiy; | |
| 388 | Can Language Models Be Biomedical Knowledge Bases? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. |
Mujeen Sung; Jinhyuk Lee; Sean Yi; Minji Jeon; Sungdong Kim; Jaewoo Kang; | |
| 389 | LayoutReader: Pre-training of Text and Layout for Reading Order Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. |
Zilong Wang; Yiheng Xu; Lei Cui; Jingbo Shang; Furu Wei; | code |
| 390 | Region Under Discussion for Visual Dialog Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we define what it means for a visual question to require dialog history and we release a subset of the Guesswhat?! |
Mauricio Mazuecos; Franco M. Luque; Jorge S?nchez; Hern?n Maina; Thomas Vadora; Luciana Benotti; | |
| 391 | Learning Grounded Word Meaning Representations on Similarity Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. |
Mariella Dimiccoli; Herwig Wendt; Pau Batlle Franch; | |
| 392 | WhyAct: Identifying Action Reasons in Lifestyle Vlogs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. |
Oana Ignat; Santiago Castro; Hanwen Miao; Weiji Li; Rada Mihalcea; | |
| 393 | Genre As Weak Supervision for Cross-lingual Dependency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we project treebank-level genre information to the finer-grained sentence level, with the goal to amplify information implicitly stored in unsupervised contextualized representations. |
Max M?ller-Eberstein; Rob van der Goot; Barbara Plank; | |
| 394 | On The Relation Between Syntactic Divergence and Zero-Shot Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. |
Ofir Arviv; Dmitry Nikolaev; Taelin Karidi; Omri Abend; | |
| 395 | Improved Latent Tree Induction with Distant Supervision Via Span Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. |
Zhiyang Xu; Andrew Drozdov; Jay Yoon Lee; Tim O?Gorman; Subendhu Rongali; Dylan Finkbeiner; Shilpa Suresh; Mohit Iyyer; Andrew McCallum; | |
| 396 | Aligning Multidimensional Worldviews and Discovering Ideological Differences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Extending the ability of word embedding models to capture the semantic and cultural characteristics of their training corpora, we propose a novel method for discovering the multifaceted ideological and worldview characteristics of communities. |
Jeremiah Milbauer; Adarsh Mathew; James Evans; | |
| 397 | Just Say No: Analyzing The Stance of Neural Dialogue Generation in Offensive Contexts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations. |
Ashutosh Baheti; Maarten Sap; Alan Ritter; Mark Riedl; | |
| 398 | Multi-Modal Open-Domain Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the goal of getting humans to engage in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. |
Kurt Shuster; Eric Michael Smith; Da Ju; Jason Weston; | |
| 399 | A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection. |
Ting-Wei Wu; Ruolin Su; Biing Juang; | |
| 400 | Zero-Shot Dialogue Disentanglement By Self-Supervised Entangled Response Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we are the first to propose a zero-shot dialogue disentanglement solution. |
Ta-Chung Chi; Alexander Rudnicky; | |
| 401 | SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user<->assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. |
Satwik Kottur; Seungwhan Moon; Alborz Geramifard; Babak Damavandi; | |
| 402 | RAST: Domain-Robust Dialogue Rewriting As Sequence Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. |
Jie Hao; Linfeng Song; Liwei Wang; Kun Xu; Zhaopeng Tu; Dong Yu; | |
| 403 | MRF-Chat: Improving Dialogue with Markov Random Fields Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel probabilistic approach using Markov Random Fields (MRF) to augment existing deep-learning methods for improved next utterance prediction. |
Ishaan Grover; Matthew Huggins; Cynthia Breazeal; Hae Won Park; | |
| 404 | Dialogue State Tracking with A Language Model Using Schema-Driven Prompting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. |
Chia-Hsuan Lee; Hao Cheng; Mari Ostendorf; | code |
| 405 | Signed Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively. |
Kayo Yin; Kenneth DeHaan; Malihe Alikhani; | |
| 406 | Consistent Accelerated Inference Via Confident Adaptive Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present CATs – Confident Adaptive Transformers – in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. |
Tal Schuster; Adam Fisch; Tommi Jaakkola; Regina Barzilay; | |
| 407 | Improving and Simplifying Pattern Exploiting Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on few-shot learning without any unlabeled data and introduce ADAPET, which modifies PET’s objective to provide denser supervision during fine-tuning. |
Derek Tam; Rakesh R. Menon; Mohit Bansal; Shashank Srivastava; Colin Raffel; | |
| 408 | Unsupervised Data Augmentation with Naive Augmentation and Without Unlabeled Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. |
David Lowell; Brian Howard; Zachary C. Lipton; Byron Wallace; | |
| 409 | Pre-train or Annotate? Domain Adaptation with A Constrained Budget Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. |
Fan Bai; Alan Ritter; Wei Xu; | |
| 410 | Lawyers Are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we focus on two widely used CSKBs, ConceptNet and GenericsKB, and establish the presence of bias in the form of two types of representational harms, overgeneralization of polarized perceptions and representation disparity across different demographic groups in both CSKBs. |
Ninareh Mehrabi; Pei Zhou; Fred Morstatter; Jay Pujara; Xiang Ren; Aram Galstyan; | |
| 411 | OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale. |
Sunipa Dev; Tao Li; Jeff M Phillips; Vivek Srikumar; | |
| 412 | Sentence-Permuted Paragraph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. |
Wenhao Yu; Chenguang Zhu; Tong Zhao; Zhichun Guo; Meng Jiang; | |
| 413 | Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial. |
Yuning Mao; Wenchang Ma; Deren Lei; Jiawei Han; Xiang Ren; | code |
| 414 | Paraphrase Generation: A Survey of The State of The Art Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. |
Jianing Zhou; Suma Bhat; | |
| 415 | Exposure Bias Versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency. |
Tianxing He; Jingzhao Zhang; Zhiming Zhou; James Glass; | |
| 416 | Generating Self-Contained and Summary-Centric Question Answer Pairs Via Differentiable Reward Imitation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. |
Li Zhou; Kevin Small; Yong Zhang; Sandeep Atluri; | |
| 417 | Unsupervised Paraphrasing with Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. |
Tong Niu; Semih Yavuz; Yingbo Zhou; Nitish Shirish Keskar; Huan Wang; Caiming Xiong; | |
| 418 | Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity. |
Hengtong Zhang; Tianhang Zheng; Yaliang Li; Jing Gao; Lu Su; Bo Li; | |
| 419 | Journalistic Guidelines Aware News Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. |
Xuewen Yang; Svebor Karaman; Joel Tetreault; Alejandro Jaimes; | |
| 420 | AESOP: Paraphrase Generation with Adaptive Syntactic Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases. |
Jiao Sun; Xuezhe Ma; Nanyun Peng; | |
| 421 | Refocusing on Relevance: Personalization in NLG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. |
Shiran Dudy; Steven Bedrick; Bonnie Webber; | |
| 422 | The Future Is Not One-dimensional: Complex Event Schema Induction By Graph Modeling for Event Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. |
Manling Li; Sha Li; Zhenhailong Wang; Lifu Huang; Kyunghyun Cho; Heng Ji; Jiawei Han; Clare Voss; | |
| 423 | Learning Constraints and Descriptive Segmentation for Subevent Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction, as well as guiding the model to make globally consistent inference. |
Haoyu Wang; Hongming Zhang; Muhao Chen; Dan Roth; | |
| 424 | ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ChemNER, an ontology-guided, distantly-supervised method for fine-grained chemistry NER to tackle these challenges. |
Xuan Wang; Vivian Hu; Xiangchen Song; Shweta Garg; Jinfeng Xiao; Jiawei Han; | |
| 425 | Moving on from OntoNotes: Coreference Resolution Model Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. |
Patrick Xia; Benjamin Van Durme; | |
| 426 | Document-level Entity-based Extraction As Template Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE). |
Kung-Hsiang Huang; Sam Tang; Nanyun Peng; | |
| 427 | Learning Prototype Representations Across Few-Shot Tasks for Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. |
Viet Lai; Franck Dernoncourt; Thien Huu Nguyen; | code |
| 428 | Lifelong Event Detection with Knowledge Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In reality, the ontology of interest may change over time, adding emergent new types or more fine-grained subtypes. We propose a new lifelong learning framework to address this challenge. |
Pengfei Yu; Heng Ji; Prem Natarajan; | |
| 429 | Modular Self-Supervision for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose decomposing document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
Sheng Zhang; Cliff Wong; Naoto Usuyama; Sarthak Jain; Tristan Naumann; Hoifung Poon; | |
| 430 | Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the use of paraphrasing as a more principled data augmentation scheme for NER unsupervised consistency training. |
Rui Wang; Ricardo Henao; | |
| 431 | Fine-grained Entity Typing Without Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Under this setting, we propose a two-step framework to train FET models. |
Jing Qian; Yibin Liu; Lemao Liu; Yangming Li; Haiyun Jiang; Haisong Zhang; Shuming Shi; | |
| 432 | Adversarial Attack Against Cross-lingual Knowledge Graph Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment. |
Zeru Zhang; Zijie Zhang; Yang Zhou; Lingfei Wu; Sixing Wu; Xiaoying Han; Dejing Dou; Tianshi Che; Da Yan; | |
| 433 | Towards Realistic Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. |
Sam Brody; Sichao Wu; Adrian Benton; | |
| 434 | Data Augmentation for Cross-Domain Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. |
Shuguang Chen; Gustavo Aguilar; Leonardo Neves; Thamar Solorio; | |
| 435 | Incorporating Medical Knowledge in BERT for Clinical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. |
Arpita Roy; Shimei Pan; | |
| 436 | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. |
Rujun Han; Xiang Ren; Nanyun Peng; | |
| 437 | Learning from Noisy Labels for Entity-Centric Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by such properties, we propose a simple co-regularization framework for entity-centric information extraction, which consists of several neural models with identical structures but different parameter initialization. |
Wenxuan Zhou; Muhao Chen; | |
| 438 | Extracting Material Property Measurement Data from Scientific Articles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe a methodology for developing an automatic property extraction framework using material solubility as the target property. |
Gihan Panapitiya; Fred Parks; Jonathan Sepulveda; Emily Saldanha; | |
| 439 | Modeling Document-Level Context for Event Detection Via Important Context Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence. |
Amir Pouran Ben Veyseh; Minh Van Nguyen; Nghia Ngo Trung; Bonan Min; Thien Huu Nguyen; | |
| 440 | Crosslingual Transfer Learning for Relation and Event Extraction Via Word Category and Class Alignments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning. |
Minh Van Nguyen; Tuan Ngo Nguyen; Bonan Min; Thien Huu Nguyen; | |
| 441 | Corpus-based Open-Domain Event Type Induction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. |
Jiaming Shen; Yunyi Zhang; Heng Ji; Jiawei Han; | |
| 442 | PDALN: Progressive Domain Adaptation Over A Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a progressive domain adaptation Knowledge Distillation (KD) approach – PDALN. |
Tao Zhang; Congying Xia; Philip S. Yu; Zhiwei Liu; Shu Zhao; | |
| 443 | Multi-Vector Attention Models for Deep Re-ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a lightweight architecture that explores this joint cost vs. accuracy trade-off based on multi-vector attention (MVA). |
Giulio Zhou; Jacob Devlin; | |
| 444 | Toward Deconfounding The Effect of Entity Demographics for Question Answering Accuracy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, … |
Maharshi Gor; Kellie Webster; Jordan Boyd-Graber; | |
| 445 | Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate what models learn from commonsense reasoning datasets. |
Kaixin Ma; Filip Ilievski; Jonathan Francis; Satoru Ozaki; Eric Nyberg; Alessandro Oltramari; | |
| 446 | Transformer Feed-Forward Layers Are Key-Value Memories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. |
Mor Geva; Roei Schuster; Jonathan Berant; Omer Levy; | |
| 447 | Connecting Attributions and QA Model Behavior on Realistic Counterfactuals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC). |
Xi Ye; Rohan Nair; Greg Durrett; | |
| 448 | How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that RNN and transformer language models exhibit structured, consistent generalization in out-of-distribution contexts. |
D. Anthony Bau; Jacob Andreas; | |
| 449 | Comparing Text Representations: A Theory-Driven Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. |
Gregory Yauney; David Mimno; | |
| 450 | Human Rationales As Attribution Priors for Explainable Stance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
Sahil Jayaram; Emily Allaway; | |
| 451 | The Stem Cell Hypothesis: Dilemma Behind Multi-Task Learning with Transformer Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this finding, we propose the Stem Cell Hypothesis to reveal the existence of attention heads naturally talented for many tasks that cannot be jointly trained to create adequate embeddings for all of those tasks. |
Han He; Jinho D. Choi; | |
| 452 | Text Counterfactuals Via Latent Optimization and Shapley-Guided Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given a textual input and a classification model, we aim to minimally alter the text to change the model’s prediction. |
Xiaoli Fern; Quintin Pope; | |
| 453 | Average Approximates First Principal Component- An Empirical Analysis on Representations from Neural Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an empirical property of these representations-average approximates first principal component. |
Zihan Wang; Chengyu Dong; Jingbo Shang; | |
| 454 | Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, it remains unclear if such an inductive bias would also improve language models’ ability to learn grammatical dependencies in typologically different languages. Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English. |
Yiwen Wang; Jennifer Hu; Roger Levy; Peng Qian; | |
| 455 | GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models. |
Weicheng Ma; Renze Lou; Kai Zhang; Lili Wang; Soroush Vosoughi; | |
| 456 | NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM). |
Tara Safavi; Jing Zhu; Danai Koutra; | |
| 457 | Instance-adaptive Training with Noise-robust Losses Against Noisy Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes novel instance-adaptive training frameworks to change single dataset-wise hyperparameters of noise resistance in such losses to be instance-wise. |
Lifeng Jin; Linfeng Song; Kun Xu; Dong Yu; | |
| 458 | Distributionally Robust Multilingual Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. |
Chunting Zhou; Daniel Levy; Xian Li; Marjan Ghazvininejad; Graham Neubig; | |
| 459 | Model Selection for Cross-lingual Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in auxiliary pivot languages. |
Yang Chen; Alan Ritter; | |
| 460 | Continual Few-Shot Learning for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples. |
Ramakanth Pasunuru; Veselin Stoyanov; Mohit Bansal; | |
| 461 | Efficient Nearest Neighbor Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take the recently proposed k-nearest neighbors language model as an example, exploring methods to improve its efficiency along various dimensions. |
Junxian He; Graham Neubig; Taylor Berg-Kirkpatrick; | |
| 462 | STraTA: Self-Training with Task Augmentation for Better Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that builds on two key ideas for effective leverage of unlabeled data. |
Tu Vu; Minh-Thang Luong; Quoc Le; Grady Simon; Mohit Iyyer; | |
| 463 | TADPOLE: Task ADapted Pre-Training Via AnOmaLy DEtection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training. |
Vivek Madan; Ashish Khetan; Zohar Karnin; | |
| 464 | Gradient-based Adversarial Attacks Against Text Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the first general-purpose gradient-based adversarial attack against transformer models. |
Chuan Guo; Alexandre Sablayrolles; Herv? J?gou; Douwe Kiela; | |
| 465 | Do Transformer Modifications Transfer Across Implementations and Applications? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. |
Sharan Narang; Hyung Won Chung; Yi Tay; Liam Fedus; Thibault Fevry; Michael Matena; Karishma Malkan; Noah Fiedel; Noam Shazeer; Zhenzhong Lan; Yanqi Zhou; Wei Li; Nan Ding; Jake Marcus; Adam Roberts; Colin Raffel; | |
| 466 | Paired Examples As Indirect Supervision in Latent Decision Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a way to leverage paired examples that provide stronger cues for learning latent decisions. |
Nitish Gupta; Sameer Singh; Matt Gardner; Dan Roth; | |
| 467 | Pairwise Supervised Contrastive Learning of Sentence Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose PairSupCon, an instance discrimination based approach aiming to bridge semantic entailment and contradiction understanding with high-level categorical concept encoding. |
Dejiao Zhang; Shang-Wen Li; Wei Xiao; Henghui Zhu; Ramesh Nallapati; Andrew O. Arnold; Bing Xiang; | |
| 468 | Muppet: Massive Multi-task Representations with Pre-Finetuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. |
Armen Aghajanyan; Anchit Gupta; Akshat Shrivastava; Xilun Chen; Luke Zettlemoyer; Sonal Gupta; | |
| 469 | Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text, to enable large-scale meta-learning in NLP. |
Trapit Bansal; Karthick Prasad Gunasekaran; Tong Wang; Tsendsuren Munkhdalai; Andrew McCallum; | |
| 470 | A Simple and Effective Method To Eliminate The Self Language Bias in Multilingual Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore this problem from a novel angle of geometric algebra and semantic space. |
Ziyi Yang; Yinfei Yang; Daniel Cer; Eric Darve; | |
| 471 | A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we investigate the linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. |
Alexander Jones; William Yang Wang; Kyle Mahowald; | |
| 472 | Frustratingly Simple But Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we experiment with six Discourse Representation Structure (DRS) semantic parsers in English, and generalize them to Italian, German and Dutch, where there are only a small number of manually annotated parses available. |
Jingfeng Yang; Federico Fancellu; Bonnie Webber; Diyi Yang; | |
| 473 | Improving Simultaneous Translation By Incorporating Pseudo-References with Fewer Reorderings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method that rewrites the target side of existing full-sentence corpora into simultaneous-style translation. |
Junkun Chen; Renjie Zheng; Atsuhito Kita; Mingbo Ma; Liang Huang; | |
| 474 | Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we evaluate several model compression techniques for QE and find that, despite their popularity in other NLP tasks, they lead to poor performance in this regression setting. |
Shuo Sun; Ahmed El-Kishky; Vishrav Chaudhary; James Cross; Lucia Specia; Francisco Guzm?n; | |
| 475 | A Large-Scale Study of Machine Translation in Turkic Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. |
Jamshidbek Mirzakhalov; Anoop Babu; Duygu Ataman; Sherzod Kariev; Francis Tyers; Otabek Abduraufov; Mammad Hajili; Sardana Ivanova; Abror Khaytbaev; Antonio Laverghetta Jr.; Bekhzodbek Moydinboyev; Esra Onal; Shaxnoza Pulatova; Ahsan Wahab; Orhan Firat; Sriram Chellappan; | |
| 476 | Analyzing The Surprising Variability in Word Embedding Stability Across Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages. |
Laura Burdick; Jonathan K. Kummerfeld; Rada Mihalcea; | |
| 477 | Rule-based Morphological Inflection Improves Neural Terminology Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. |
Weijia Xu; Marine Carpuat; | |
| 478 | Data and Parameter Scaling Laws for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. |
Mitchell A Gordon; Kevin Duh; Jared Kaplan; | |
| 479 | Good-Enough Example Extrapolation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To operationalize this question, I propose a simple data augmentation protocol called good-enough example extrapolation (GE3). |
Jason Wei; | |
| 480 | Learning to Selectively Learn for Weakly-supervised Paraphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. |
Kaize Ding; Dingcheng Li; Alexander Hanbo Li; Xing Fan; Chenlei Guo; Yang Liu; Huan Liu; | |
| 481 | Effective Convolutional Attention Network for Multi-label Clinical Document Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. |
Yang Liu; Hua Cheng; Russell Klopfer; Matthew R. Gormley; Thomas Schaaf; | |
| 482 | Contrastive Code Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ContraCode: a contrastive pre-training task that learns code functionality, not form. |
Paras Jain; Ajay Jain; Tianjun Zhang; Pieter Abbeel; Joseph Gonzalez; Ion Stoica; | code |
| 483 | IGA: An Intent-Guided Authoring Assistant Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases text according to fine-grained author specifications. |
Simeng Sun; Wenlong Zhao; Varun Manjunatha; Rajiv Jain; Vlad Morariu; Franck Dernoncourt; Balaji Vasan Srinivasan; Mohit Iyyer; | |
| 484 | Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. |
Zichao Wang; Andrew Lan; Richard Baraniuk; | |
| 485 | Navigating The Kaleidoscope of COVID-19 Misinformation Using Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By conducting a systematic investigation, we show that: (i) the deep Transformer-based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution. |
Yuanzhi Chen; Mohammad Hasan; | |
| 486 | Detecting Health Advice in Medical Research Literature Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. |
Yingya Li; Jun Wang; Bei Yu; | |
| 487 | A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Semantic Feature-wise transformation Relation Network (SFRN) that exploits the multiple components of ASAG datasets more effectively. |
Zhaohui Li; Yajur Tomar; Rebecca J. Passonneau; | |
| 488 | Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present approaches to estimate content-aware bibliometrics to quantitatively measure the scholarly impact of a publication. |
Saurav Manchanda; George Karypis; | |
| 489 | A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. |
Sunghyun Park; Han Li; Ameen Patel; Sidharth Mudgal; Sungjin Lee; Young-Bum Kim; Spyros Matsoukas; Ruhi Sarikaya; | |
| 490 | Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. |
Naoya Inoue; Harsh Trivedi; Steven Sinha; Niranjan Balasubramanian; Kentaro Inui; | |
| 491 | FewshotQA: A Simple Framework for Few-shot Learning of Question Answering Tasks Using Pre-trained Text-to-text Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. |
Rakesh Chada; Pradeep Natarajan; | |
| 492 | Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval |