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.