Ph.D. candidate, the Ohio State University
Constructing natural language interfaces (NLIs) that allow humans to acquire knowledge and complete tasks using natural language has been a long-term pursuit. This is challenging because human language can be very ambiguous and complex. Moreover, existing NLIs typically provide no means for human users to validate the system decisions; even if they could, most systems do not learn from user feedback to avoid similar mistakes in their future deployment.
In this talk, I will introduce my research about building interactive NLIs, where an NLI is formulated as an intelligent agent that can interactively and proactively request human validation when it feels uncertain. I instantiate this idea in the task of semantic parsing (e.g., parsing natural language into a SQL query). In the first part of the talk, I will present a general interactive semantic parsing framework [EMNLP 2019], and describe an imitation learning algorithm (with theoretical analysis) for improving semantic parsers continually from user interaction [EMNLP 2020]. In the second part, I will further talk about a generalized problem of editing tree-structured data under user interaction, e.g., how to edit the Abstract Syntax Trees of computer programs based on user edit specifications [ICLR 2021]. Finally, I will conclude by outlining future work around interactive NLIs and human-centered NLP/AI in general.
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