NDLI: Dialog act tagging using graphical models

Content Provider	IEEE Xplore Digital Library
Author	Gang Ji Bilmes, J.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA (Gang Ji; Bilmes, J.)
Abstract	Detecting discourse patterns, such as dialog acts (DAs), is an important factor for processing spoken conversations and meetings. Different techniques, such as hidden Markov models and neural networks, have been used to tag dialog acts in the past. A full analysis of dialog act tagging using different generative and conditional dynamic Bayesian networks (DBNs) is performed, where both conventional switching n-grams and factored language models (FLMs) are used as DBN edge implementations. Our tests on the ICSI meeting recorder dialog act (MRDA) corpus show that the factored language model implementations are better than the switching n-gram approach. Our results also show that by using virtual evidence, the label bias problem in conditional models can be avoided. Also, we find that, on a corpus such as MRDA, using the dialog acts of previous sentences to help predict current words does not improve our conditional model.
Sponsorship	IEEE Signal Processing Soc
File Size	283020
File Format	PDF
ISBN	0780388747
ISSN	15206149
DOI	10.1109/ICASSP.2005.1415043
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tagging Graphical models Hidden Markov models Neural networks Bayesian methods Natural languages Performance analysis Testing Predictive models Speech recognition
Content Type	Text
Resource Type	Article

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