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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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Language (Re)modelling: Towards Embodied Language Understanding

Ronen TamariChen ShaniTom HopeDafna Shahaf
2020
ACL

While natural language understanding (NLU) is advancing rapidly, today’s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency,… 

Nakdan: Professional Hebrew Diacritizer

Avi ShmidmanShaltiel ShmidmanMoshe KoppelYoav Goldberg
2020
ACL

We present a system for automatic diacritization of Hebrew text. The system combines modern neural models with carefully curated declarative linguistic knowledge and comprehensive manually… 

Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

Erfan Sadeqi AzerDaniel KhashabiAshish SabharwalDan Roth
2020
ACL

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 several known… 

Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

Shauli RavfogelYanai ElazarHila GonenYoav Goldberg
2020
ACL

The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present… 

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… 

pyBART: Evidence-based Syntactic Transformations for IE

Aryeh TiktinskyYoav GoldbergReut Tsarfaty
2020
ACL

Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These… 

QuASE: Question-Answer Driven Sentence Encoding.

Hangfeng HeQiang NingDan Roth
2020
ACL

Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)?… 

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

Maarten SapEric HorvitzYejin ChoiJames W. Pennebaker
2020
ACL

We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release… 

S2ORC: The Semantic Scholar Open Research Corpus

Kyle LoLucy Lu WangMark E NeumannDaniel S. Weld
2020
ACL

We introduce S2ORC, a large contextual citation graph of English-language academic papers from multiple scientific domains; the corpus consists of 81.1M papers, 380.5M citation edges, and associated… 

SciREX: A Challenge Dataset for Document-Level Information Extraction

Sarthak JainMadeleine van ZuylenHannaneh HajishirziIz Beltagy
2020
ACL

Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to…