5th International Conference on Spoken Language Processing
Recognizing the dialogue act(s) performed by means of an utterance involves combining top-down expectations about the next likely `move' in a dialogue with bottom-up information extracted from the speech signal. We compared two ways of generating expectations: one which makes the expectations depend only on the previous act (as in a bigram model), and one which also takes into account the fact that individual dialogue acts play a role as part of larger conversational structures (`games'). Our models were built by training over the HCRC MapTask corpus using the LTG implementation of maximum entropy estimation. We achieved an accuracy of 38.6% using bigrams, of 50.6% taking game structure into account; adding information about speaker change resulted in an accuracy of 41.8% with bigrams, 54% with game structure. These results indicate that exploiting game structure does lead to improved expectations.
Bibliographic reference. Poesio, Massimo / Mikheev, Andrei (1998): "The predictive power of game structure in dialogue act recognition: experimental results using maximum entropy estimation", In ICSLP-1998, paper 0606.