Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
“I’m Not Mad”: Commonsense Implications of Negation and Contradiction
Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the…
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs…
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering
Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access…
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation…
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to a given history. We present the MultiTalk Dataset, a corpus of over 320,000 sentences of written conversational dialog…
Paragraph-Level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences beyond what is stated in the text explicitly. A recent model, COMeT, can generate such inferences along several…
Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes
As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a field of study that…
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
Commonsense AI has long been seen as a near impossible goal—until recently. Now, research interest has sharply increased with an influx of new benchmarks and models. We propose two new ways to…
GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation
Leaderboards have eased model development for many NLP datasets by standardizing their evaluation and delegating it to an independent external repository. Their adoption, however, is so far limited…
On-the-Fly Attention Modularization for Neural Generation
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: generated text is repetitive, generic, selfinconsistent, and lacking…