Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
SeGAN: Segmenting and Generating the Invisible
Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and…
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
We study the task of directly modelling a visually intelligent agent. Computer vision typically focuses on solving various subtasks related to visual intelligence. We depart from this standard…
Structured Set Matching Networks for One-Shot Part Labeling
Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large…
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new…
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research pur- poses (PeerRead v1), providing…
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated),…
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first…
Annotation Artifacts in Natural Language Inference Data
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails,…
Content-Based Citation Recommendation
We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank…
Deep Contextualized Word Representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across…