What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations

Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael R. Lyu, Irwin King


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.
Anthology ID:
Q19-1017
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
March
Year:
2019
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
267–281
Language:
URL:
https://www.aclweb.org/anthology/Q19-1017
DOI:
10.1162/tacl_a_00267
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PDF:
http://aclanthology.lst.uni-saarland.de/Q19-1017.pdf
Video:
 https://vimeo.com/385265124