Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
UnQovering Stereotyping Biases via Underspecified Questions
While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework…
Rearrangement: A Challenge for Embodied AI
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as…
ABNIRML: Analyzing the Behavior of Neural IR Models
Numerous studies have demonstrated the effectiveness of pretrained contextualized language models such as BERT and T5 for ad-hoc search. However, it is not wellunderstood why these methods are so…
GO FIGURE: A Meta Evaluation of Factuality in Summarization
Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. Recent work has focused on building evaluation models to verify the…
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
Conditional text generation often requires lexical constraints, i.e., which words should or shouldn’t be included in the output text. While the dominant recipe for conditional text generation has…
Paraphrasing vs Coreferring: Two Sides of the Same Coin
We study the potential synergy between two different NLP tasks, both confronting lexical variability: identifying predicate paraphrases and event coreference resolution. First, we used annotations…
Generative Data Augmentation for Commonsense Reasoning
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading:…
Learning Object Detection from Captions via Textual Scene Attributes
Object detection is a fundamental task in computer vision, requiring large annotated datasets that are difficult to collect, as annotators need to label objects and their bounding boxes. Thus, it is…
Scene Graph to Image Generation with Contextualized Object Layout Refinement
Generating high-quality images from scene graphs, that is, graphs that describe multiple entities in complex relations, is a challenging task that attracted substantial interest recently. Prior work…