Learning agents that interact with humans

When:
Friday, March 10, 2017, 3:00 pm - 4:00 pm PDTiCal
Where:
11th Flr Conf Room-CR #1135
This event is open to the public.
Type:
NL Seminar
Speaker:
He He (Stanford)
Description:

Abstract: The future of virtual assistants, self-driving cars, and smart homes require intelligent agents that work intimately with users. Instead of passively following orders given by users, an interactive agent must actively collaborate with people through communication, coordination, and user-adaptation. In this talk, I will present our recent work towards building agents that interact with humans. First, we propose a symmetric collaborative dialogue setting in which two agents, each with some private knowledge, must communicate in natural language to achieve a common goal. We present a human-human dialogue dataset that poses new challenges to existing models, and propose a neural model with dynamic knowledge graph embedding. Second, we study the user-adaptation problem in quizbowl - a competitive, incremental question-answering game. We show that explicitly modeling of different human behavior leads to more effective policies that exploits sub-optimal players. I will conclude by discussing opportunities and open questions in learning interactive agents.

bio: He He is a post-doc at Stanford University, working with Percy Liang. Prior to Stanford, she earned her Ph.D. in Computer Science at the University of Maryland, College Park, advised by Hal Daumé III and Jordan Boyd-Graber. Her interests are at the interface of machine learning and natural language processing. She develops algorithms that acquire information dynamically and do inference incrementally, with an emphasis on problems in natural language processing. She has worked on dependency parsing, simultaneous machine translation, question answering, and more recently dialogue systems.

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