Events Calendar

CS Colloquium: Antoine Bosselut (University of Washington) - Neuro-symbolic Representations for Commonsense Knowledge and Reasoning

Speaker: Antoine Bosselut, University of Washington

Talk Title: Neuro-symbolic Representations for Commonsense Knowledge and Reasoning

Series: CS Distinguished Lectures

Abstract: Situations described using natural language are richer than what humans explicitly communicate. For example, the sentence "She pumped her fist" connotes many potential auspicious causes. For machines to understand natural language, they must be able to reason about the commonsense inferences that underlie explicitly stated information. In this talk, I will present work on combining traditional symbolic knowledge and reasoning techniques with modern neural representations to endow machines with these capacities.

First, I will describe COMET, an approach for learning commonsense knowledge about unlimited situations and concepts using transfer learning from language to knowledge. Second, I will demonstrate how these neural knowledge representations can dynamically construct symbolic graphs of contextual commonsense knowledge, and how these graphs can be used for interpretable, generalized reasoning. Finally, I will discuss current and future research directions on conceptualizing NLP as commonsense simulation, and the impact of this framing on challenging open-ended tasks such as story generation.

This lecture satisfies requirements for CSCI 591: Research Colloquium

Biography: Antoine Bosselut is a PhD Student at the University of Washington advised by Professor Yejin Choi, and a student researcher at the Allen Institute for Artificial Intelligence. His research focuses on building systems for commonsense knowledge representation and reasoning that combine the strengths of modern neural and traditional symbolic methods. He was also a student researcher on the Deep Learning team at Microsoft Research from 2017 to 2018. He is supported by an AI2 Key Scientific Challenges award.

Host: Xiang Ren

Location: Olin Hall of Engineering (OHE) - 132

Audiences: Everyone Is Invited

Contact: Assistant to CS chair