Corpus-Based Discourse Understanding in Spoken Dialogue Systems

Corpus-based Discourse Understanding in Spoken Dialogue Systems Ryuichiro Higashinaka and Mikio Nakano and Kiyoaki Aikawa† NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation 3-1 Morinosato Wakamiya Atsugi, Kanagawa 243-0198, Japan {rh,nakano}@atom.brl.ntt.co.jp, aik@idea.brl.ntt.co.jp Abstract This paper concerns the discourse under- standing process in spoken dialogue sys- tems. This process enables the system to understand user utterances based on the context of a dialogue. Since multiple can- didates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understanding result after each user ut- terance. By holding multiple candidates for understanding results and resolving the ambiguity as the dialogue progresses, the discourse understanding accuracy can be improved. This paper proposes a method for resolving this ambiguity based on sta- tistical information obtained from dia- logue corpora. Unlike conventional meth- ods that use hand-crafted rules, the pro- posed method enables easy design of the discourse understanding process. Experi- ment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple can- didates for understanding results is effec- tive. †Currently with the School of Media Science, Tokyo Uni- versity of Technology, 1404-1 Katakuracho, Hachioji, Tokyo 192-0982, Japan. 1 Introduction For spoken dialogue systems to correctly understand user intentions to achieve certain tasks while con- versing with users, the dialogue state has to be ap- propriately updated (Zue and Glass, 2000) after each user utterance. Here, a dialogue state means all the information that the system possesses concern- ing the dialogue. For example, a dialogue state in- cludes intention recognition results after each user utterance, the user utterance history, the system ut- terance history, and so forth. Obtaining the user in- tention and the content of an utterance using only the single utterance is called speech understanding, and updating the dialogue state based on both the previ- ous utterance and the current dialogue state is called discourse understanding. In general, the result of speech understanding can be ambiguous, because it is currently difficult to uniquely decide on a single speech recognition result out of the many recogni- tion candidates available, and because the syntac- tic and semantic analysis process normally produce multiple hypotheses. The system, however, has to be able to uniquely determine the understanding result after each user utterance in order to respond to the user. The system therefore must be able to choose the appropriate speech understanding result by re- ferring to the dialogue state. Most conventional systems uniquely determine the result of the discourse understanding, i.e., the dialogue state, after each user utterance. However, multiple dialogue states are created from the current dialogue state and the speech understanding results corresponding to the user utterance, which leads to ambiguity. When this ambiguity is ignored, the dis- course understanding accuracy is likely to decrease. Our idea for improving the discourse understanding accuracy is to make the system hold multiple dia- logue states after a user utterance and use succeed- ing …

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