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Active Learning for Dialogue Act Classification
Number of Authors: 3
2011 (English)Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2011, 9.
National Category
Computer and Information Science
URN: urn:nbn:se:ri:diva-16192OAI: diva2:1038216
INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association
Available from: 2016-10-18 Created: 2016-10-18

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Gambäck, BjörnTäckström, Oscar
Computer and Information Science

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