Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?
While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from the real-world data…
Quantifying the narrative flow of imagined versus autobiographical stories.
Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story.…
Generating Sequences by Learning to Self-Correct
Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesir-able content. Language models,…
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and…
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…
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses , that improves…
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
Figurative language (e.g., “he flew like the wind”) is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that…
Webly Supervised Concept Expansion for General Purpose Vision Models
General purpose vision (GPV) systems [25] are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and…
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…
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation
We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression…