Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both…
VISALOGY: Answering Visual Analogy Questions
In this paper, we study the problem of answering visual analogy questions. These questions take the form of image A is to image B as image C is to what. Answering these questions entails discovering…
BDD-Guided Clause Generation
Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic…
Semantic Role Labeling for Process Recognition Questions
We consider a 4th grade level question answering task. We focus on a subset involving recognizing instances of physical, biological, and other natural processes. Many processes involve similar…
Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears…
Exploring Markov Logic Networks for Question Answering
Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from…
VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases
How can we know whether a statement about our world is valid. For example, given a relationship between a pair of entities e.g., 'eat(horse, hay)', how can we know whether this relationship is true…
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models…
Learning Knowledge Graphs for Question Answering through Conversational Dialog
We describe how a question-answering system can learn about its domain from conversational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores…
Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering
Monolingual alignment models have been shown to boost the performance of question answering systems by "bridging the lexical chasm" between questions and answers. The main limitation of these…