Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take…
Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference…
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…
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,…
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…
Crowdsourcing Multiple Choice Science Questions
We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality,…
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,…
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…
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or handengineered mention detector. The…
How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets
Large scale machine learning produces massive datasets whose items are often associated with a confidence level and can thus be ranked. However, computing the precision of these resources requires…