RI: Small: Linguistic Semantics and Discourse from Leaky Distant Supervision

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This project studies novel algorithms for building artificial intelligence (AI) systems that can learn to improve their performance with a human in the loop. Many recent AI successes are driven by large, expensive and difficult-to-collect datasets. This yields systems that are deep, but narrow. The goal of this project is to build technology that will allow AI systems to learn from their interactions with people. The project focuses on key applications related to natural language understanding: building technology to understand the meanings of individual sentences, and integrate those meanings into the meaning of a discourse or dialog. One specific application pursued herein relates to extracting biomedical knowledge from text, which will pave the way to helping biomedical researchers develop novel hypotheses. The work will fund students from underrepresented groups in STEM, and encourage cross-disciplinary education at the graduate and undergraduate levels. Finally, the work will be communicated to the public not just with scientific papers, but internationally through social media and locally through visits to middle schools and high schools. Natural language processing (and other fields of artificial intelligence) have had enormous success by training supervised learning systems on large labeled datasets ("corpora"). Unfortunately, curating such corpora is infeasible except for very specific problems. This happens either because it is too expensive, or it is too difficult to get human labelers to agree on an annotation standard. Instead of relying solely on human labeled data, this project develops algorithms that can learn from human interaction. These systems can continually improve their performance based on downstream performance supervision, often with a human in the loop. This work leverages recent developments on the structured contextual bandits learning framework which provides a theoretically grounded and computationally efficient way in which to develop novel approaches to distant supervision. This resulting learning techniques will push advances in natural language understanding: semantic parsing and discourse interpretation. Furthermore, the underlying imitation learning technology is broadly applicable, including novel applications to recurrent neural network models. To aid adoption by the research community, code and data from this project will be released open source.