Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Learning Generalizable Visual Representations via Interactive Gameplay
A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the…
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
We present the ARC-DA dataset, a direct-answer (“open response”, “freeform”) version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been influential in the community,…
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…
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…
Optimizing AI for Teamwork
In many high-stakes domains such as criminal justice, finance, and healthcare, AI systems may recommend actions to a human expert responsible for final decisions, a context known as AI-advised…
On Generating Extended Summaries of Long Documents
Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in…
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…
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…
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…