12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Active Learning for Dialogue Act Classification

Björn Gambäck, Fredrik Olsson, Oscar Täckström

SICS, Sweden

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result.

Full Paper

Bibliographic reference.  Gambäck, Björn / Olsson, Fredrik / Täckström, Oscar (2011): "Active learning for dialogue act classification", In INTERSPEECH-2011, 1329-1332.