Recently there has been growing interest in using dialog acts to characterize human-human and human-machine dialogs. This paper reports on our experience in the annotation and the automatic detection of dialog acts in human-human spoken dialog corpora. Our work is based on two hypotheses: first, word position is more important than the exact word in identifying the dialog act; and second, there is a strong grammar constraining the sequence of dialog acts. A memory based learning approach has

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... n used to detect dialog acts. In a first set of experiments the number of utterances per turn is known, and in a second set, the number of utterances is hypothesized using a language model for utterance boundary detection. In order to verify our first hypothesis, the model trained on a French corpus was tested on a corpus for a similar task in English and for a second French corpus from a different domain. A correct dialog act detection rate of about 84% is obtained for the same domain and language condition and about 75% for the cross-language and cross-domain conditions.