The Conference on Empirical Methods in Natural Language Processing (EMNLP) is one of the top natural language processing conferences in the world. In 2020, it is to be held virtually due to covid-19 pandemic.
An innovation for EMNLP 2020 is a new acceptance category, which will allow for more high quality papers (short and long) to be accepted than usual. EMNLP 2020 is creating a new sister publication, Findings of ACL: EMNLP 2020 (hereafter Findings), which will serve as an online companion publication for papers that are not accepted for publication in the main conference, but nonetheless have been assessed by the programme committee as solid work with sufficient substance, quality and novelty to warrant publication.
Papers published on the main track are put in a different page: Main Track Paper Highlights.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
An innovation for EMNLP 2020 is a new acceptance category, which will allow for more high quality papers (short and long) to be accepted than usual. EMNLP 2020 is creating a new sister publication, Findings of ACL: EMNLP 2020 (hereafter Findings), which will serve as an online companion publication for papers that are not accepted for publication in the main conference, but nonetheless have been assessed by the programme committee as solid work with sufficient substance, quality and novelty to warrant publication.
Papers published on the main track are put in a different page: Main Track Paper Highlights.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.