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Computer Science @ Rochester
Friday, April 29, 2011
12:00 PM
CSB 703
Regina Barzilay
MIT
Learning to Behave by Reading
In this talk, I will address the problem of grounding linguistic analysis in control applications, such as game playing. We assume access to natural language documents that describe the desired behavior of a control algorithm (e.g., game strategy guides). Our goal is to demonstrate that knowledge automatically extracted from such documents can improve performance of the target application.

First, I will present a reinforcement learning algorithm for learning to map natural language instructions to executable actions. This technique has enabled automation of tasks that until now have required human participation --- for example, automatically configuring software by consulting how-to guides. Next, I will present a Monte-Carlo search algorithm for game playing that incorporates information from game strategy guides. In this framework, the task of text interpretation is formulated as a probabilistic model that is trained based on feedback from Monte-Carlo search. When applied to the Civilization strategy game, a language-empowered player outperforms its traditional counterpart by a significant margin.

This is joint work with Branavan, Harr Chen, David Silver and Luke Zettlemoyer.