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Amazon Alexa Prize Discourse Model & Coreference Resolution

Presenters: Jeshwanth Bheemanpally, Phillip Lee, Cecilia Li, Angela Ramirez, Eduardo Zamora

Project Mentor: Dr. Marilyn Walker & Dr. Adwait Ratnaparkhi, UCSC

Abstract: Our goal is to create a coreference resolution model that can identify entities being referred to in a social bot's user utterance using information found in our discourse model. We implement both a rule-based, neural, and ensemble model, and compare how they perform on a set of pronominal types: it, they, that, she, he, her, him, his, and hers. To enhance our models, we harness the information found in our discourse model that tracks and holds information of which entities are currently in focus in the conversation, independently of whether they are realized by a pronoun or a definite referring expression or a proper noun, and independently of whether they are introduced into the discourse by the user or the system. By doing so, we hope to improve the system's ability to acknowledge the entity being referred to by the user and respond with more salience.