b'Matt Gardner'

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Alongside huge volumes of research on deep learning models in NLP in the...
07/27/2021 ∙ by Anna Rogers, et al. ∙ 2 ∙ share

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Tailor: Generating and Perturbing Text with Semantic Controls

Making controlled perturbations is essential for various tasks (e.g., da...
07/15/2021 ∙ by Alexis Ross, et al. ∙ 6 ∙ share

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Enforcing Consistency in Weakly Supervised Semantic Parsing

The predominant challenge in weakly supervised semantic parsing is that ...
07/13/2021 ∙ by Nitish Gupta, et al. ∙ 0 ∙ share

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A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Readers of academic research papers often read with the goal of answerin...
05/07/2021 ∙ by Pradeep Dasigi, et al. ∙ 21 ∙ share

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Documenting the English Colossal Clean Crawled Corpus

As language models are trained on ever more text, researchers are turnin...
04/18/2021 ∙ by Jesse Dodge, et al. ∙ 7 ∙ share

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Generative Context Pair Selection for Multi-hop Question Answering

Compositional reasoning tasks like multi-hop question answering, require...
04/18/2021 ∙ by Dheeru Dua, et al. ∙ 5 ∙ share

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Learning with Instance Bundles for Reading Comprehension

When training most modern reading comprehension models, all the question...
04/18/2021 ∙ by Dheeru Dua, et al. ∙ 13 ∙ share

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Competency Problems: On Finding and Removing Artifacts in Language Data

Much recent work in NLP has documented dataset artifacts, bias, and spur...
04/17/2021 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Paired Examples as Indirect Supervision in Latent Decision Models

Compositional, structured models are appealing because they explicitly d...
04/05/2021 ∙ by Nitish Gupta, et al. ∙ 0 ∙ share

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Mitigating False-Negative Contexts in Multi-document QuestionAnswering with Retrieval Marginalization

Question Answering (QA) tasks requiring information from multiple docume...
03/22/2021 ∙ by Ansong Ni, et al. ∙ 0 ∙ share

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Learning from Task Descriptions

Typically, machine learning systems solve new tasks by training on thous...
11/16/2020 ∙ by Orion Weller, et al. ∙ 0 ∙ share

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IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

Humans often have to read multiple documents to address their informatio...
11/13/2020 ∙ by James Ferguson, et al. ∙ 0 ∙ share

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MedICaT: A Dataset of Medical Images, Captions, and Textual References

Understanding the relationship between figures and text is key to scient...
10/12/2020 ∙ by Sanjay Subramanian, et al. ∙ 18 ∙ share

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Improving Compositional Generalization in Semantic Parsing

Generalization of models to out-of-distribution (OOD) data has captured ...
10/12/2020 ∙ by Inbar Oren, et al. ∙ 0 ∙ share

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MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics

Posing reading comprehension as a generation problem provides a great de...
10/07/2020 ∙ by Anthony Chen, et al. ∙ 0 ∙ share

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Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

High-quality and large-scale data are key to success for AI systems. How...
10/06/2020 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Understanding Mention Detector-Linker Interaction for Neural Coreference Resolution

Coreference resolution is an important task for discourse-level natural ...
09/20/2020 ∙ by Zhaofeng Wu, et al. ∙ 0 ∙ share

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Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Answering questions that involve multi-step reasoning requires decomposi...
07/01/2020 ∙ by Ben Bogin, et al. ∙ 7 ∙ share

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Matt Gardner

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