Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is…
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project
AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even in 2016, the best…
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation
We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation…
A Dataset for Tracking Entities in Open Domain Procedural Text
We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using…
A Simple Yet Strong Pipeline for HotpotQA
State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition,…
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions
Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all…
Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This…
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering.
Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To…
More Bang for Your Buck: Natural Perturbation for Robust Question Answering
While recent models have achieved human-level scores on many NLP datasets, we observe that they are considerably sensitive to small changes in input. As an alternative to the standard approach of…
OCNLI: Original Chinese Natural Language Inference
Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has…