Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
A Simple Yet Strong Pipeline for HotpotQA
State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition,…
UnifiedQA: Crossing Format Boundaries With a Single QA System
Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit…
Fact or Fiction: Verifying Scientific Claims
We introduce the task of scientific fact-checking. Given a corpus of scientific articles and a claim about a scientific finding, a fact-checking model must identify abstracts that support or refute…
TLDR: Extreme Summarization of Scientific Documents
We introduce TLDR generation for scientific papers, a new automatic summarization task with high source compression, requiring expert background knowledge and complex language understanding. To…
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of…
"You are grounded!": Latent Name Artifacts in Pre-trained Language Models
Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models. We focus on artifacts associated with the representation of given names (e.g.,…
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension. Consider a passage describing a rabbit's life-cycle: humans can easily…
Unsupervised Commonsense Question Answering with Self-Talk
Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world…
Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This…
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
Mirroring the success of masked language models, vision-and-language counterparts like VILBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative…