Joint Modeling of Content and Discourse Relations in Dialogues

Kechen Qin, Lu Wang, Joseph Kim


Abstract
We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members’ understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.
Anthology ID:
P17-1090
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
974–984
Language:
URL:
https://www.aclweb.org/anthology/P17-1090
DOI:
10.18653/v1/P17-1090
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1090.pdf
Video:
 https://vimeo.com/234957724