Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions
The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition…
The Extraordinary Failure of Complement Coercion Crowdsourcing
Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion…
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…