Title: Learning semantics and pragmatics from dialogue history Speaker: Matthew Stone (Rutgers) Time: 2:15pm-3:30pm, Friday, February 18 Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Abstract: When two people talk together, they work together to reach a shared understanding of one another. This collaborative effort seems to be crucial to the robustness of natural communication, and to people's capacity to learn from and adapt to one another's use of language. Work on dialogue systems increasingly focuses on improving systems' skills at interaction and collaboration, with the goal of allowing machines to talk through new ideas flexibly and successfully. In this talk, I demonstrate some of the ways such collaborative skills can improve the functionality of dialogue systems. In particular, I describe a system that learns from experience to understand users better in situated task-oriented dialogue. The system accumulates training examples for ambiguity resolution by tracking the fates of alternative interpretations across dialogue, including subsequent clarificatory episodes initiated collaboratively by the system itself. We realize this approach by building maximum entropy models over abductive interpretations in a referential communication task. The resulting model correctly resolves 81% of ambiguities left unresolved by an initial handcrafted baseline. A key innovation is that our method draws exclusively on a system’s own skills and experience and requires no human annotation. This is joint work with David DeVault, USC. Bio: Matthew Stone is Associate Professor in the Computer Science Department and the Center for Cognitive Science at Rutgers. He got his PhD in 1998 from the University of Pennsylvania. He studies computational models of conversation, particularly models of utterance production, for intelligent agents that interact naturally with human partners. He serves on the editorial board of the journal Artificial Intelligence and served as program co-chair for the 2007 North American Association for Computational Linguistics Human Language Technology Conference (NAACL HLT).