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Home » Stanford AI Lab Papers at EMNLP/CoNLL 2021

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!

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