Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, researchers, and…
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse…
A Simple and Effective Model for Answering Multi-span Questions
Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for…
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering.
Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To…
More Bang for Your Buck: Natural Perturbation for Robust Question Answering
While recent models have achieved human-level scores on many NLP datasets, we observe that they are considerably sensitive to small changes in input. As an alternative to the standard approach of…
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the…
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…