Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Domain-Targeted, High Precision Knowledge Extraction
Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream…
Distilling Task Knowledge from How-To Communities
Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However,…
Verb Physics: Relative Physical Knowledge of Actions and Objects
Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated…
Detecting English Writing Styles For Non Native Speakers
This paper presents the first attempt, up to our knowledge, to classify English writing styles on this scale with the challenge of classifying day to day language written by writers with different…
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This map- ping allows them to effectively leverage the information con- tained in large, formal…
Probabilistic Neural Programs
We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling…
Designing AI Systems that Obey Our Laws and Values
Operational AI systems (for example, self-driving cars) need to obey both the law of the land and our values. We propose AI oversight systems ("AI Guardians") as an approach to addressing this…
Tables as Semi-structured Knowledge for Question Answering
Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated…
Adaptive Concentration Inequalities for Sequential Decision Problems
A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees. We introduce Hoeffding-like…
Examples are not enough. Learn to criticize! Criticism for Interpretability
Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the…