Keywords:utterance intention classification, spoken dialogue system , corpus
In a human-human conversation, a listener frequently conveys various intentions explicitly/implicitly to the speaker with reflexive short responses. The speaker recognizes the intentions of these feedbacks and changes the utterance plans to make the communication more smooth and efficient. These functions are expected to be useful for human-system conversation also, but when human face the system they do not always give the same feedback as they did with human. We investigated the feedback phenomena of human users against our system which is designed to transfer a massive amount of information like news articles by spoken dialogue. First, user's intentions of feedbacks that can affect the behavior of the system were classified from the viewpoint of roles in the progress of conversation: presence/absence of a user's interest, comprehension state, release/keep turn, and so on. Then, using our news delivery conversation system, we gathered user's short responses, and labeled the intention through listening test by the subjects. We also attempted automatic identification of those intentions. As a result, in the current system, it is found that there are many feedbacks to convey doubt and automatic identification of them is possible stably, but there are not so many other feedbacks.