Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Commonsense Knowledge in Machine Intelligence
There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual…
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…
Dynamic Entity Representations in Neural Language Models
Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically…
Zero-Shot Activity Recognition with Verb Attribute Induction
In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such…
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,…
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…
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…
Deep Semantic Role Labeling: What Works and What's Next
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use…
Visual Semantic Planning using Deep Successor Representations
A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual…