Mixed Depth Representations for Dialog Processing

Abstract

We describe our work on developing a general purpose tutoring system that will allow students to practice their decision-making skills in a number of domains. The tutoring system, B2, supports mixed-initiative natural language interaction. The natural language processing and knowledge representation components are also general purpose—which leads to a tradeoff between the limitations of superficial processing and syntactic representations and the difficulty of deeper methods and conceptual representations. Our solution is to use a mixed-depth representation, one that encodes syntactic and conceptual information in the same structure. As a result, we can use the same representation framework to produce a detailed representation of requests (which tend to be well-specified) and to produce a partial representation of questions (which tend to require more inference about the context). Moreover, the representations use the same knowledge representation framework that is used to reason about discourse processing and domain information—so that the system can reason with (and about) the utterances, if necessary.