Does Active Learning Help Automatic Dialog Act Tagging in Meeting Data?

TitleDoes Active Learning Help Automatic Dialog Act Tagging in Meeting Data?
Publication TypeConference Paper
Year of Publication2005
AuthorsVenkataraman, A., Liu Y., Shriberg E., & Stolcke A.
Published inProceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005)
Page(s)2777-2780
Other Numbers1499
Abstract

Knowledge of Dialog Acts (DAs) is important for the automatic understanding and summarization of meetings. Current approaches rely on a lot of hand labeled data to train automatic taggers. One approach that has been successful in reducing the amount of training data in other areas of NLP is active learning. We ask if active learning with lexical cues can help for this task and this domain. To better address this question, we explore active learning for two different types of DA models -- hidden Markov models (HMMs) and maximum entropy (maxent).

URLhttp://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/eurospeech2005-active.pdf
Bibliographic Notes

Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005), Lisboa, Portugal, pp. 2777-2780

Abbreviated Authors

A. Venkataraman, Y. Liu, E. Shriberg, and A. Stolcke

ICSI Research Group

Speech

ICSI Publication Type

Article in conference proceedings