Mixed Depth Representations for Dialog Processing

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 tradeo between the limitations of super cial processing and syntactic representations and the di culty of deeper methods and conceptual representations. Students' utterances may be short and ambiguous, requiring extensive reasoning about the domain or the discourse model to fully resolve. However, full disambiguation is rarely necessary. 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-speci ed) 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.