Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature
Reviewing the literature to understand relevant threads of past work is a critical part of research and vehicle for learning. However, as the scientific literature grows the challenges for users to…
SciFact-Open: Towards open-domain scientific claim verification
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic…
A Dataset of Alt Texts from HCI Publications
Figures in scientifc publications contain important information and results, and alt text is needed for blind and low vision readers to engage with their content. We conduct a study to characterize…
Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction
Extracting temporal relationships between pairs of events in texts is a crucial yet challenging problem for natural language understanding. Depending on the distance between the events, models must…
Few-Shot Self-Rationalization with Natural Language Prompts
Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however,…
Literature-Augmented Clinical Outcome Prediction
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive…
Long Context Question Answering via Supervised Contrastive Learning
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g.,…
MultiVerS: Improving scientific claim verification with weak supervision and full-document context
The scientific claim verification task requires an NLP system to label scientific documents which Support or Refute an input claim, and to select evidentiary sentences (or rationales) justifying…
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show…
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy…