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).
Cite as: Venkataraman, A., Liu, Y., Shriberg, E., Stolcke, A. (2005) Does active learning help automatic dialog act tagging in meeting data? Proc. Interspeech 2005, 2777-2780, doi: 10.21437/Interspeech.2005-819
@inproceedings{venkataraman05_interspeech,
author={Anand Venkataraman and Yang Liu and Elizabeth Shriberg and Andreas Stolcke},
title={{Does active learning help automatic dialog act tagging in meeting data?}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2777--2780},
doi={10.21437/Interspeech.2005-819}
}