Interaction and Natural Language Learning

Abstract: This talk focuses on the challenges and opportunities that interactions provide for natural language learning. I will show how collaborative interactions enable continual learning, where agentive systems improve over time through interaction. I will describe a game-like environment that instantiates collaborative interactions with natural language coordination, and show how it creates a contextual bandit learning scenario for language production (i.e., generation). I will also discuss language-conditioned reinforcement learning (RL), a research area that remains challenging to pursue despite its promise for both research and application. A key challenge hindering progress is computing rewards that require resolving language semantics. I will describe a language-conditioned RL benchmark using a new approach for reward computation, striking a balance between research accessibility and retaining the complexities and nuances of natural language.

Bio: Yoav Artzi is an Associate Professor in the Department of Computer Science and Cornell Tech at Cornell University. His research focuses on developing learning methods for natural language understanding and generation in automated interactive systems. He received an NSF CAREER award, and his work was acknowledged by awards and honorable mentions at ACL, EMNLP, NAACL, and IROS. Yoav holds a B.Sc. from Tel Aviv University and a Ph.D. from the University of Washington.