Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
TransOMCS: From Linguistic Graphs to Commonsense Knowledge
Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations,…
Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses
Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known…
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not…
Procedural Reading Comprehension with Attribute-Aware Context Flow
Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading…
Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations
We present a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of:…
GenericsKB: A Knowledge Base of Generic Statements
We present a new resource for the NLP community, namely a large (3.5M+ sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon dioxide from the atmosphere", collected from…
Probing Natural Language Inference Models through Semantic Fragments
Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity…
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based…
What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering
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…
Reasoning Over Paragraph Effects in Situations
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…