Speaker: Ziyu Yao
Time: Thurs 02/27/2020, 4pm-5pm
Location: Dreese Lab 480

Title: Learning a Semantic Parser from User Interaction

Abstract:
Training a machine learning model usually requires extensive supervision. Particularly for semantic parsers that aim to convert a natural language utterance into a domain-specific meaning representation (e.g., a SQL query), large-scale annotations from domain experts can be very costly. In our ongoing work, we study continually training a deployed semantic parser from end-user feedback, allowing the system better harness the vast store of potential training signals over its lifetime and adapt itself towards more practical user feeds. To this end, we present the first interactive system that proactively requests for intermediate, fine-grained feedback from user interaction and improves itself via an annotation-efficient imitation learning algorithm. On two text-to-SQL benchmark datasets, we first demonstrate that our system can continually improve a semantic parser by simply leveraging interaction feedback from non-expert users. Compared with existing feedback-based online learning approaches, our system enables more efficient learning, i.e., enhancing a parser’s performance with fewer user annotations. We finally show a theoretical analysis discussing the annotation efficiency advantage of our algorithm.

Bio: Ziyu Yao is a fifth-year Ph.D. student in the CSE department, advised by Prof. Huan Sun. Her current research interests include building interactive and interpretable natural language interfaces, as well as general applications of deep learning and reinforcement learning to interdisciplinary domains. She has been publishing papers at ACL/EMNLP/WWW/AAAI and was a research intern at Microsoft Research, Redmond.