5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

The Predictive Power of Game Structure in Dialogue Act Recognition: Experimental Results Using Maximum Entropy Estimation

Massimo Poesio (1), Andrei Mikheev (2)

(1) University of Edinburgh, HCRC, UK
(2) Harlequin, UK

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.

Full Paper

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.