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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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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… 

RNN Architecture Learning with Sparse Regularization

Jesse DodgeRoy SchwartzHao PengNoah A. Smith
2019
EMNLP

Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning… 

PaLM: A Hybrid Parser and Language Model

Hao PengRoy SchwartzNoah A. Smith
2019
EMNLP

We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised constituency parser… 

Topics to Avoid: Demoting Latent Confounds in Text Classification

Sachin KumarShuly WintnerNoah A. SmithYulia Tsvetkov
2019
EMNLP

Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize… 

Efficient Navigation with Language Pre-training and Stochastic Sampling

Xiujun LiChunyuan LiQiaolin XiaYejin Choi
2019
EMNLP

Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and… 

Social IQA: Commonsense Reasoning about Social Interactions

Maarten SapHannah RashkinDerek ChenYejin Choi
2019
EMNLP

We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social… 

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

Oyvind TafjordMatt GardnerKevin LinPeter Clark
2019
EMNLP

We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen with a higher SPF… 

COSMOS QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Lifu HuangRonan Le BrasChandra BhagavatulaYejin Choi
2019
EMNLP

Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper,…