Publication Details
Title: Does Active Learning Help Automatic Dialog Act Tagging in Meeting Data?
Author: A. Venkataraman, Y. Liu, E. Shriberg, and A. Stolcke
Group: Speech
Date: September 2005
PDF: http://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/eurospeech2005-active.pdf
Overview:
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).
Bibliographic Information:
Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005), Lisboa, Portugal, pp. 2777-2780
Bibliographic Reference:
A. Venkataraman, Y. Liu, E. Shriberg, and A. Stolcke. Does Active Learning Help Automatic Dialog Act Tagging in Meeting Data?. Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005), Lisboa, Portugal, pp. 2777-2780, September 2005
Author: A. Venkataraman, Y. Liu, E. Shriberg, and A. Stolcke
Group: Speech
Date: September 2005
PDF: http://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/eurospeech2005-active.pdf
Overview:
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).
Bibliographic Information:
Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005), Lisboa, Portugal, pp. 2777-2780
Bibliographic Reference:
A. Venkataraman, Y. Liu, E. Shriberg, and A. Stolcke. Does Active Learning Help Automatic Dialog Act Tagging in Meeting Data?. Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005), Lisboa, Portugal, pp. 2777-2780, September 2005