Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
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…
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine,…
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…
Incorporating Ethics into Artificial Intelligence
This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then…
Interactive Visualization for Linguistic Structure
We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides…
LCNN: Lookup-based Convolutional Neural Network
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for…
Learning to Predict Citation-Based Impact Measures
Citations implicitly encode a community's judgment of a paper's importance and thus provide a unique signal by which to study scientific impact. Efforts in understanding and refining this signal are…
Learning What is Essential in Questions
Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper…
Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors
While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions…
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations:…