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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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Discovering Neural Wirings

Mitchell WortsmanAli FarhadiMohammad Rastegari
2019
NeurIPS

The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined… 

Analyzing Compositionality in Visual Question Answering

Sanjay SubramanianSameer SinghMatt Gardner
2019
NeurIPS • ViGIL Workshop

Since the release of the original Visual Question Answering (VQA) dataset, several newer datasets for visual reasoning have been introduced, often with the express intent of requiring systems to… 

What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

Tushar KhotAshish SabharwalPeter Clark
2019
EMNLP

Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial information is… 

Evaluating Question Answering Evaluation

Anthony ChenGabriel StanovskySameer SinghMatt Gardner
2019
EMNLP • MRQA Workshop

As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to… 

Reasoning Over Paragraph Effects in Situations

Kevin LinOyvind TafjordPeter ClarkMatt Gardner
2019
EMNLP • MRQA Workshop

A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we… 

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

Dheeru DuaAnanth GottumukkalaAlon TalmorMatt Gardner
2019
EMNLP • MRQA Workshop

Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study… 

On Making Reading Comprehension More Comprehensive

Matt GardnerJonathan BerantHannaneh HajishirziSewon Min
2019
EMNLP • MRQA Workshop

Machine reading comprehension, the task of evaluating a machine’s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted… 

Knowledge Enhanced Contextual Word Representations

Matthew E. PetersMark NeumannRobert L. Loganand Noah A. Smith
2019
EMNLP

Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those… 

Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

Pradeep DasigiNelson F. LiuAna MarasovicMatt Gardner
2019
EMNLP

Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential… 

Show Your Work: Improved Reporting of Experimental Results

Jesse DodgeSuchin GururanganDallas CardNoah A. Smith
2019
EMNLP

Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this…