Natural language response generation in mixed-initiative dialogs using task goals and dialog acts

This paper presents our approach towards natural language response generation for mixed-initiative dialogs in the CUHK Restaurants domain. Our experimental corpus consists of about 4000 customer requests and waiter responses. Every request/response utterance is annotated with its task goal (TG) and dialog act (DA). The variable pair {TG, DA} is used to represent the dialog state. Our approach involves a set of corpus-derived dialog state transition rules in the form of {TG, DA}request ∆ {TG, DA}response. These rules encode the communication goal(s) and initiatives of the request/ response. Another set of hand-designed rules associate each response dialog state with one or more text generation templates. Upon testing, our system parses the input customer request for concept categories and from these infers the TG and DA using trained Belief Networks. Application of the dialog state transition rules and text generation templates automatically generates a (virtual) waiter response. Ten subjects were invited to interact with the system. Performance evaluation based on Grice's maxims gave a mean score of 4 on a five-point Likert scale and a task completion rate of at least 90%.