What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
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 .
Authors: Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael Rung-Tsong Lyu, Irwin King
Published in: Transactions of the Association for Computational Linguistics (2019)