SIGdial 2013

14th Annual SIGdial Meeting on Discourse and Dialogue

Training an integrated sentence planner on user dialogue

Brian McMahan, Matthew Stone

An appealing methodology for natural language generation in dialogue systems is to train the system to match a target corpus. We show how users can provide such a corpus as a natural side effect of interacting with a prototype system, when the system uses mixed-initiative interaction and a reversible architecture to cover a domain familiar to users. We experiment with integrated problems of sentence planning and realization in a referential communication task. Our model learns general and context-sensitive patterns to choose descriptive content, vocabulary, syntax and function words, and improves string match with user utterances to 85.8% from a handcrafted baseline of 54.4%.