Corpus-based Discourse Understanding in Spoken Dialogue Systems 
 This paper concerns the discourse understanding process in spoken dialogue systems . This process enables the system to understand user utterances based on the context of a dialogue . Since multiple candidates 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 utterance . 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 statistical information obtained from dialogue corpora . Unlike conventional methods that use hand-crafted rules , the proposed method enables easy design of the discourse understanding process . Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective. 