@article{zeng-etal-2019-say,
title = "What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations",
author = "Zeng, Jichuan and
Li, Jing and
He, Yulan and
Gao, Cuiyun and
Lyu, Michael R. and
King, Irwin",
editor = "Lee, Lillian and
Johnson, Mark and
Roark, Brian and
Nenkova, Ani",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1017",
doi = "10.1162/tacl_a_00267",
pages = "267--281",
abstract = "This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. Our data sets and code are available at: \url{http://github.com/zengjichuan/Topic_Disc}.",
}
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<abstract>This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. Our data sets and code are available at: http://github.com/zengjichuan/Topic_Disc.</abstract>
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%0 Journal Article
%T What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
%A Zeng, Jichuan
%A Li, Jing
%A He, Yulan
%A Gao, Cuiyun
%A Lyu, Michael R.
%A King, Irwin
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F zeng-etal-2019-say
%X This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. Our data sets and code are available at: http://github.com/zengjichuan/Topic_Disc.
%R 10.1162/tacl_a_00267
%U https://aclanthology.org/Q19-1017
%U https://doi.org/10.1162/tacl_a_00267
%P 267-281
Markdown (Informal)
[What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations](https://aclanthology.org/Q19-1017) (Zeng et al., TACL 2019)
ACL