Stanford AI Lab Papers at EMNLP/CoNLL 2021

The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) will take place next week, colocated with CoNLL 2021. We’re excited to share all the work from SAIL that will be presented, and you’ll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that’s happening at Stanford!

List of Accepted Papers

Calibrate your listeners! Robust communication-based training for pragmatic speakers

Authors: Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman
Contact: rewang@stanford.edu
Links: Paper | Video
Keywords: language generation, pragmatics, communication-based training, calibration, uncertainty

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text

Authors: Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, Christopher Ré
Contact: mvarma2@stanford.edu
Links: Paper | Video
Keywords: named entity disambiguation, biomedical text, rare entities, data integration

ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts

Authors: Yuta Koreeda, Christopher D. Manning
Contact: koreeda@stanford.edu
Links: Paper | Website
Keywords: natural language inference, contract, law, legal, dataset
Venue: The Findings of EMNLP 2021

The Emergence of the Shape Bias Results from Communicative Efficiency

Authors: Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, Romain Laroche
Contact: portelan@stanford.edu
Links: Paper | Website
Keywords: emergent communication, shape bias, multi-agent reinforcement learning, language learning, language acquisition
Conference: CoNLL

LM-Critic: Language Models for Unsupervised Grammatical Error Correction

Authors: Michihiro Yasunaga, Jure Leskovec, Percy Liang.
Contact: myasu@cs.stanford.edu
Links: Paper | Blog Post | Website
Keywords: language model, grammatical error correction, unsupervised translation

Sensitivity as a complexity measure for sequence classification tasks

Authors: Michael Hahn, Dan Jurafsky, Richard Futrell
Contact: mhahn2@stanford.edu
Links: Paper
Keywords: decision boundaries, computational complexity

Distributionally Robust Multilingual Machine Translation

Authors: Chunting Zhou*, Daniel Levy*, Marjan Ghazvininejad, Xian Li, Graham Neubig
Contact: daniel.levy0@gmail.com
Keywords: machine translation, robustness, distribution shift, dro, cross-lingual transfer

Learning from Limited Labels for Long Legal Dialogue

Authors: Jenny Hong, Derek Chong, Christopher D. Manning
Contact: jennyhong@cs.stanford.edu
Keywords: legal nlp, information extraction, weak supervision

Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser

Authors: Yuta Koreeda, Christopher D. Manning
Contact: koreeda@stanford.edu
Links: Paper | Website
Keywords: legal, preprocessing
Workshop: Natural Legal Language Processing Workshop

We look forward to seeing you at EMNLP/CoNLL 2021!