565 citations · 2.3k across the 68 of their papers we have counts for
9 papers · 2 filters
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
Kyle Richardson, Ashish Sabharwal
Open-domain question answering (QA) is known to involve several underlying knowledge and reasoning challenges, but are models actually learning such knowledge when trained on bench…
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses
Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal +1
Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from s…
QASC: A Dataset for Question Answering via Sentence Composition
Tushar Khot, Peter Clark, Michal Guerquin +2
Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Comp…
What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering
Tushar Khot, Ashish Sabharwal, Peter Clark
Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial…
Probing Natural Language Inference Models through Semantic Fragments
Kyle Richardson, Hai Hu, Lawrence S. Moss +1
Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, an…
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project
Peter Clark, Oren Etzioni, Daniel Khashabi +11
AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy, but the rich variety of standardized exams has remained a landmark challenge. Even in…