Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense benchmark datasets. However, building machines with common-sense to compose…
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on…
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce "Data Maps"---a…
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube
Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks. Features are learned via prediction of grounded…
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of…
Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq
High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly…
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…
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…
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…
Improving Compositional Generalization in Semantic Parsing
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures…