@inproceedings{qin-etal-2017-joint,
title = "Joint Modeling of Content and Discourse Relations in Dialogues",
author = "Qin, Kechen and
Wang, Lu and
Kim, Joseph",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1090",
doi = "10.18653/v1/P17-1090",
pages = "974--984",
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.",
}
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%0 Conference Proceedings
%T Joint Modeling of Content and Discourse Relations in Dialogues
%A Qin, Kechen
%A Wang, Lu
%A Kim, Joseph
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qin-etal-2017-joint
%X 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.
%R 10.18653/v1/P17-1090
%U https://aclanthology.org/P17-1090
%U https://doi.org/10.18653/v1/P17-1090
%P 974-984
Markdown (Informal)
[Joint Modeling of Content and Discourse Relations in Dialogues](https://aclanthology.org/P17-1090) (Qin et al., ACL 2017)
ACL