Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Tropical Cirrus in Global Storm‐Resolving Models: 1. Role of Deep Convection
Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top‐of‐atmosphere radiation balance and stratospheric water vapor budget.…
DREAM: Improving Situational QA by First Elaborating the Situation
When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests that they form a mental picture of that…
Inherently Explainable Reinforcement Learning in Natural Language
We focus on the task of creating a reinforcement learning agent that is inherently explainable—with the ability to produce immediate local explanations by thinking out loud while performing a task…
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing
Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a…
MERLOT: Multimodal Neural Script Knowledge Models
As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future. We introduce MERLOT, a model…
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
Constructing benchmarks that test the abilities of modern natural language un1 derstanding models is difficult – pre-trained language models exploit artifacts in 2 benchmarks to achieve human…
NaturalProofs: Mathematical Theorem Proving in Natural Language
Understanding and creating mathematics using natural mathematical language – the mixture of symbolic and natural language used by humans – is a challenging and important problem for driving progress…
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE , a comparison measure…
FLEX: Unifying Evaluation for Few-Shot NLP
Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental…
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
We present CORA, a Cross-lingual Open-Retrieval Answer Generation model that can answer questions across many languages even when language-specific annotated data or knowledge sources are…