The 58th annual meeting of the Association for Computational Linguistics is being hosted virtually this week. We’re excited to share all the work from SAIL that’s being 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

Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation

Authors: Weixin Liang, James Zou, Zhou Yu
Contact: wxliang@stanford.edu
Keywords: dialog, automatic dialog evaluation, user experience

Contextual Embeddings: When Are They Worth It?

Authors: Simran Arora, Avner May, Jian Zhang, Christopher Ré
Contact: simarora@stanford.edu
Links: Paper | Video
Keywords: contextual embeddings, pretraining, benefits of context

Enabling Language Models to Fill in the Blanks

Authors: Chris Donahue, Mina Lee, Percy Liang
Contact: cdonahue@cs.stanford.edu
Links: Paper | Blog Post | Video
Keywords: natural language generation, infilling, fill in the blanks, language models

ExpBERT: Representation Engineering with Natural Language Explanations

Authors: Shikhar Murty, Pang Wei Koh, Percy Liang
Contact: smurty@cs.stanford.edu
Links: Paper | Video
Keywords: language explanations, bert, relation extraction, language supervision

Finding Universal Grammatical Relations in Multilingual BERT

Authors: Ethan A. Chi, John Hewitt, Christopher D. Manning
Contact: ethanchi@cs.stanford.edu
Links: Paper | Blog Post
Keywords: analysis, syntax, multilinguality

Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds

Authors: Kawin Ethayarajh
Contact: kawin@stanford.edu
Links: Paper
Keywords: fairness, bias, equal opportunity, ethics, uncertainty

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Authors: Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Ré
Contact: chami@stanford.edu
Links: Paper | Video
Keywords: knowledge graphs, hyperbolic embeddings, link prediction

Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports

Authors: Yuhao Zhang, Derek Merck, Emily Bao Tsai, Christopher D. Manning, Curtis P. Langlotz
Contact: yuhao.zhang@stanford.edu
Links: Paper
Keywords: nlp, text summarization, reinforcement learning, medicine, radiology report

Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

Authors: Yun Tang, Jing Huang, Guangtao Wang, Xiaodong He and Bowen Zhou
Contact: jhuang18@stanford.edu
Links: Paper | Video
Keywords: orthogonal transforms, knowledge graph embedding

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

Authors: Dan Iter , Kelvin Guu , Larry Lansing, Dan Jurafsky
Contact: daniter@stanford.edu
Links: Paper
Keywords: discourse coherence, language model pretraining

Robust Encodings: A Framework for Combating Adversarial Typos

Authors: Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang
Contact: erjones@stanford.edu
Links: Paper
Keywords: nlp, robustness, adversarial robustness, typos, safe ml

SenseBERT: Driving Some Sense into BERT

Authors: Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Schwarz, Amnon Shashua, Yoav Shoham
Contact: shoham@cs.stanford.edu
Links: Paper | Blog Post
Keywords: language models, semantics

Shaping Visual Representations with Language for Few-shot Classification

Authors: Jesse Mu, Percy Liang, Noah Goodman
Contact: muj@stanford.edu
Links: Paper
Keywords: grounding, language supervision, vision, few-shot learning, meta-learning, transfer

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Authors: Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, Christopher D. Manning
Contact: pengqi@cs.stanford.edu
Links: Paper
Keywords: natural language processing, multilingual, data-driven, neural networks

Theoretical Limitations of Self-Attention in Neural Sequence Models

Authors: Michael Hahn
Contact: mhahn2@stanford.edu
Links: Paper
Keywords: theory, transformers, formal languages

Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Authors: Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, and Monica S. Lam
Contact: gcampagn@stanford.edu
Links: Paper
Keywords: dialogue state tracking, multiwoz, zero-shot, data programming, pretraining

We look forward to seeing you at ACL 2020!