activity
20182021
most citedBoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

209 citations · 265 across the 5 of their papers we have counts for

collaborators

11 papers

cs.CL2021

Decontextualization: Making Sentences Stand-Alone

Eunsol Choi, Jennimaria Palomaki, Matthew Lamm +3

Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts…

cs.CL2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

Sewon Min, Jordan Boyd-Graber, Chris Alberti +50

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…

cs.CL202017 cited

Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking

Thibault Févry, Nicholas FitzGerald, Livio Baldini Soares +1

In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-ar…

cs.CL2020

Entities as Experts: Sparse Memory Access with Entity Supervision

Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald +2

We focus on the problem of capturing declarative knowledge about entities in the learned parameters of a language model. We introduce a new model - Entities as Experts (EAE) - that…

cs.CL2020

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages

Jonathan H. Clark, Eunsol Choi, Michael Collins +4

Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA---a question answering dataset covering 11 typologically dive…

cs.CL202022 cited

Learning Cross-Context Entity Representations from Text

Jeffrey Ling, Nicholas FitzGerald, Zifei Shan +4

Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent…