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Classification of Discourse Functions of Affirmative Words in Spoken Dialogue

Hirschberg, Julia Bell; Gravano, Agustin; Benus, Stefan; Mitchell, Shira; Vovsha, Ilia

We present results of a series of machine learning experiments that address the classification of the discourse function of single affirmative cue words such as alright, okay and mm-hm in a spoken dialogue corpus. We suggest that a simple discourse/sentential distinction is not sufficient for such words and propose two additional classification sub-tasks: identifying (a) whether such words convey acknowledgment or agreement, and (b) whether they cue the beginning or end of a discourse segment. We also study the classification of each individual word into its most common discourse functions. We show that models based on contextual features extracted from the time-aligned transcripts approach the error rate of trained human aligners.

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More About This Work

Academic Units
Computer Science
Publisher
Proceedings of Interspeech 2007
Published Here
July 14, 2013

Notes

Presentation Powerpoint slides are available at http://hdl.handle.net/10022/AC:P:21264

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