Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to…
IKE - An Interactive Tool for Knowledge Extraction
Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publicly available tools exist. In…
Instructable Intelligent Personal Agent
Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their…
Metaphor as a Medium for Emotion: An Empirical Study
It is generally believed that a metaphor tends to have a stronger emotional impact than a literal statement; however, there is no quantitative study establishing the extent to which this is true.…
Moving Beyond the Turing Test with the Allen AI Science Challenge
The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism…
Much Ado About Time: Exhaustive Annotation of Temporal Data
Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These…
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the…
PDFFigures 2.0: Mining Figures from Research Papers
Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or…
Probabilistic Models for Learning a Semantic Parser Lexicon
We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality…
Question Answering via Integer Programming over Semi-Structured Knowledge
Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval.…