Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal…
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates…
WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation
Despite important progress in the area of intelligent systems, most such systems still lack commonsense knowledge that appears crucial for enabling smarter, more human-like decisions. In this paper,…
AI zooms in on highly influential citations
The number of times a paper is cited is a poor proxy for its impact (see P. Stephan et al. Nature 544, 411–412; 2017). I suggest relying instead on a new metric that uses artificial intelligence…
Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
We introduce the task of Multi-Modal Machine Comprehension (M3C), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question…
Asynchronous Temporal Fields for Action Recognition
Actions are more than just movements and trajectories: we cook to eat and we hold a cup to drink from it. A thorough understanding of videos requires going beyond appearance modeling and…
Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple…
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers
We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions--the math…
Bidirectional Attention Flow for Machine Comprehension
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been…
Commonly Uncommon: Semantic Sparsity in Situation Recognition
Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the…