Abstract

Many applications of natural language processing need to understand text from the rich context in which it occurs and present information in a new context. Interpreting the rich context of a sentence, either conversation history, social context, or preceding contents in the document, is challenging yet crucial to understand the sentence. In the first part of the talk, we study the context-reduction process by defining the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. Typically a sentence taken out of a context is unintelligible, but decontextualization recovers key pieces of information and make sentences stand alone. We demonstrate the utility of this process, as a preprocessing for open-domain question answering and for generating an informative and concise answer to an information-seeking query. In the latter half of the talk, we focus on building models to integrate rich context to interpret single utterances more accurately. We study the challenges of interpreting rich context in question answering, by first integrating conversational history and by integrating entity information. Together, these works show how modeling interaction between text and the rich context in which it occurs can improve performances of NLP systems.

Biography

Eunsol Choi is an assistant professor in the computer science department at the University of Texas at Austin. Her research focuses on natural language processing, various ways to recover semantics from unstructured text. Recently, her research focused on question answering and entity analysis. Prior to UT, she was a visiting faculty researcher at Google AI. She received a Ph.D. from the University of Washington, working with Luke Zettlemoyer and Yejin Choi. She received an undergraduate degree in mathematics and computer science at Cornell University. She is a recipient Facebook Research Fellowship and has co-organized many workshops related to question answering at NLP and ML venues.