Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
A Controllable QA-based Framework for Decontextualization
Many real-world applications require surfacing extracted snippets to users, whether motivated by assistive tools for literature surveys or document cross-referencing, or needs to mitigate and…
Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction
Mathematical symbol definition extraction is important for improving scholarly reading interfaces and scholarly information extraction (IE). However, the task poses several challenges: math symbols…
Decomposing Complex Queries for Tip-of-the-tongue Retrieval
When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs -- complex queries that describe content…
Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery
Literature-Based Discovery (LBD) aims to discover new scientific knowledge by mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise relations between discrete…
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive…
Embedding Recycling for Language Models
Training and inference with large neural models is expensive. However, for many application domains, while new tasks and models arise frequently, the underlying doc-uments being modeled remain…
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty and workload when…
S2abEL: A Dataset for Entity Linking from Scientific Tables
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When applied to tables in…
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of…
ComLittee: Literature Discovery with Personal Elected Author Committees
In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches…