Context Management with Topics for Spoken Dialogue Systems

In this paper we discuss the use of discourse context in spoken dialogue systems and argue that the knowledge of the domain, modelled with the help of dialogue topics is important in maintaining robustness of the system and improving recognition accuracy of spoken utterances. We propose a topic model which consists of a domain model, structured into a topic tree, and the Predict-Support algorithm which assigns topics to utterances on the basis of the topic transitions described in the topic tree and the words recognized in the input utterance. The algorithm uses a probabilistic topic type tree and mutual information between the words and different topic types, and gives recognition accuracy of 78.68% and precision of 74.64%. This makes our topic model highly comparable to discourse models which are based on recognizing dialogue acts.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Kristiina Jokinen, Hideki Tanaka
• Keywords: ACL 1998, ACL 2007, Topic Model, Topic Tree, Topic Types