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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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Break It Down: A Question Understanding Benchmark

Tomer WolfsonMor GevaAnkit GuptaJonathan Berant
2020
TACL

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning… 

Injecting Numerical Reasoning Skills into Language Models

Mor GevaAnkit GuptaJonathan Berant
2020
ACL

Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn… 

Obtaining Faithful Interpretations from Compositional Neural Networks

Sanjay SubramanianBen BoginNitish GuptaMatt Gardner
2020
ACL

Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional… 

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Alon TalmorJonathan HerzigNicholas LourieJonathan Berant
2019
NAACL

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant… 

The Web as a Knowledge-base for Answering Complex Questions

Alon TalmorJonathan Berant
2018
NAACL

Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple…