Matt Gardner

Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...
05/02/2020 ∙ by Sanjay Subramanian, et al. ∙ 22 ∙ share

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Compositional Questions Do Not Necessitate Multi-hop Reasoning

Multi-hop reading comprehension (RC) questions are challenging because t...
06/07/2019 ∙ by Sewon Min, et al. ∙ 6 ∙ share

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Simple and Effective Multi-Paragraph Reading Comprehension

We consider the problem of adapting neural paragraph-level question answ...
10/29/2017 ∙ by Christopher Clark, et al. ∙ 0 ∙ share

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Crowdsourcing Multiple Choice Science Questions

We present a novel method for obtaining high-quality, domain-targeted mu...
07/19/2017 ∙ by Johannes Welbl, et al. ∙ 0 ∙ share

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Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Traditional semantic parsers map language onto compositional, executable...
07/12/2016 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Deep contextualized word representations

We introduce a new type of deep contextualized word representation that ...
02/15/2018 ∙ by Matthew E. Peters, et al. ∙ 0 ∙ share

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AllenNLP: A Deep Semantic Natural Language Processing Platform

This paper describes AllenNLP, a platform for research on deep learning ...
03/20/2018 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

Many natural language questions require recognizing and reasoning with q...
11/20/2018 ∙ by Oyvind Tafjord, et al. ∙ 0 ∙ share

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Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language...
03/21/2019 ∙ by Nelson F. Liu, et al. ∙ 0 ∙ share

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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reading comprehension has recently seen rapid progress, with systems mat...
03/01/2019 ∙ by Dheeru Dua, et al. ∙ 0 ∙ share

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Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Research on parsing language to SQL has largely ignored the structure of...
05/15/2019 ∙ by Ben Bogin, et al. ∙ 0 ∙ share

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Grammar-based Neural Text-to-SQL Generation

The sequence-to-sequence paradigm employed by neural text-to-SQL models ...
05/30/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability...
08/16/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

Machine comprehension of texts longer than a single sentence often requi...
08/16/2019 ∙ by Pradeep Dasigi, et al. ∙ 0 ∙ share

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Global Reasoning over Database Structures for Text-to-SQL Parsing

State-of-the-art semantic parsers rely on auto-regressive decoding, emit...
08/29/2019 ∙ by Ben Bogin, et al. ∙ 0 ∙ share

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Do NLP Models Know Numbers? Probing Numeracy in Embeddings

The ability to understand and work with numbers (numeracy) is critical f...
09/17/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QuaRTz, for reasoning...
09/08/2019 ∙ by Oyvind Tafjord, et al. ∙ 0 ∙ share

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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Neural NLP models are increasingly accurate but are imperfect and opaque...
09/19/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Question Answering is a Format; When is it Useful?

Recent years have seen a dramatic expansion of tasks and datasets posed ...
09/25/2019 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Neural Module Networks for Reasoning over Text

Answering compositional questions that require multiple steps of reasoni...
12/10/2019 ∙ by Nitish Gupta, et al. ∙ 0 ∙ share

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ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering researc...
12/29/2019 ∙ by Dheeru Dua, et al. ∙ 0 ∙ share

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Break It Down: A Question Understanding Benchmark

Understanding natural language questions entails the ability to break do...
01/31/2020 ∙ by Tomer Wolfson, et al. ∙ 0 ∙ share

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Multi-Step Inference for Reasoning Over Paragraphs

Complex reasoning over text requires understanding and chaining together...
04/06/2020 ∙ by Jiangming Liu, et al. ∙ 0 ∙ share

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Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...
04/06/2020 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

A critical part of reading is being able to understand the temporal rela...
05/01/2020 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Matt Gardner

Featured Co-authors

Noah A. Smith

Luke Zettlemoyer

Yoav Goldberg

Dan Roth

Hao Wu

Hannaneh Hajishirzi

Jonathan Berant

Sameer Singh

Yonatan Belinkov

Sujian Li

Ashish Sabharwal

Obtaining Faithful Interpretations from Compositional Neural Networks

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Simple and Effective Multi-Paragraph Reading Comprehension

Crowdsourcing Multiple Choice Science Questions

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Deep contextualized word representations

AllenNLP: A Deep Semantic Natural Language Processing Platform

QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

Linguistic Knowledge and Transferability of Contextual Representations

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Grammar-based Neural Text-to-SQL Generation

Universal Adversarial Triggers for NLP

Universal Adversarial Triggers for Attacking and Analyzing NLP

Reasoning Over Paragraph Effects in Situations

Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

Global Reasoning over Database Structures for Text-to-SQL Parsing

Do NLP Models Know Numbers? Probing Numeracy in Embeddings

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Question Answering is a Format; When is it Useful?

Neural Module Networks for Reasoning over Text

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Break It Down: A Question Understanding Benchmark

Multi-Step Inference for Reasoning Over Paragraphs

Evaluating NLP Models via Contrast Sets

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions