Context Management with Topics for Spoken Dialogue Systems

Context Management with Topics for Spoken Dialogue Systems Kristiina Jokinen and Hideki Tanaka and Akio Yokoo ATR Interpreting Telecommunications Research Laboratories 2-2 Hikaridai, Seika-cho, Soraku-gun Kyoto 619-02 Japan email : {kj okinen[tanakah[ayokoo}~itl, air. co. jp Abstract In this paper we discuss the use of discourse con- text in spoken dialogue systems and argue that the knowledge of the domain, modelled with the help of dialogue topics is important in maintaining robust- ness of the system and improving recognition accu- racy 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 infor- mation between the words and different topic types, and gives recognition accuracy of 78.68% and preci- sion of 74.64%. This makes our topic model highly comparable to discourse models which are based on recognizing dialogue acts. 1 Introduction One of the fragile points in integrated spoken lan- guage systems is the erroneous analyses of the initial speech input. 1 The output of a speech recognizer has direct influence on the performance of other mod- ules of the system (dealing with dialogue manage- ment, translation, database search, response plan- ning, etc.), and the initial inaccuracy usually gets accumulated in the later stages of processing. Per- formance of speech recognizers can be improved by tuning their language model and lexicon, but prob- lems still remain with the erroneous ranking of the best paths: information content of the selected ut- terances may be wrong. It is thus essential to use contextual information to compensate various errors in the output, to provide expectations of what will be said next and to help to determine the appropri- ate dialogue state. However, negative effects of an inaccurate context have also been noted: cumulative error in discourse context drags performance of the system below the rates it would achieve were contextual information 1 Alexandersson (1996) remarks that with a 3000 word lex- icon, a 75 % word accuracy means that in practice the word lattice does not contain the actually spoken sentence, not used (Qu et al., 1996; Church and Gale, 1991). Successful use of context thus presupposes appro- priate context management: (1) features that define the context are relevant for the processing task, and (2) construction of the context is accurate. In this paper we argue in favour of using one type of contextual information, topic information, to maintain robustness of a spoken language sys- tem. Our model deals with the information content of utterances, and defines the context in terms of topic types, related to the current domain knowl- edge and represented in the form of a topic tree. To update the context with topics we introduce the Predict-Support algorithm which selects utterance topics on the basis of topic transitions described in the topic tree and …

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