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

What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations Jichuan Zeng1∗ Jing Li2 ∗ Yulan He3 Cuiyun Gao1 Michael R. Lyu1 Irwin King1 1Department of Computer Science and Engineering The Chinese University of Hong Kong, HKSAR, China 2Tencent AI Lab, Shenzhen, China 3Department of Computer Science, University of Warwick, UK 1{jczeng, cygao, lyu, king}@cse.cuhk.edu.hk 2ameliajli@tencent.com, 3yulan.he@warwick.ac.uk Abstract This paper presents an unsupervised frame- work for jointly modeling topic content and discourse behavior in microblog conversa- tions. 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 opin- ions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Fur- ther study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. 1 Introduction The last decade has witnessed the revolution of communication, where the ‘‘kitchen table conversations’’ have been expanded to public dis- cussions on online platforms. As a consequence, in our daily life, the exposure to new information and the exchange of personal opinions have been mediated through microblogs, one popular online platform genre (Bakshy et al., 2015). The flourish of microblogs has also led to the sheer quantity of user-created conversations emerging every day, exposing individuals to superfluous infor- mation. Facing such an unprecedented number of conversations relative to limited attention of indi- viduals, how shall we automatically extract the critical points and make sense of these microblog conversations? ∗This work was partially conducted in Jichuan Zeng’s internship in Tencent AI Lab. Corresponding author: Jing Li. 1Our data sets and code are available at: http:// github.com/zengjichuan/Topic_Disc. Toward key focus understanding of a conver- sation, previous work has shown the benefits of discourse structure (Li et al., 2016b; Qin et al., 2017; Li et al., 2018), which shapes how messages interact with each other, forming the discussion flow, and can usefully reflect salient topics raised in the discussion process. After all, the topical content of a message naturally occurs in context of the conversation discourse and hence should not be modeled in isolation. Conversely, the extracted topics can reveal the purpose of participants and further facilitate the understanding of their dis- course behavior (Qin et al., 2017). Further, the joint effects of topics and discourse will contribute to better understanding of social media conversa- tions, benefiting downstream tasks such as the management of discussion topics and discourse behavior of social chatbots (Zhou et al., 2018) and the prediction of user engagements for conversa- tion recommendation (Zeng et al., 2018b). To illustrate how the topics and discourse inter- play in a conversation, Figure 1 displays a snip- pet of Twitter conversation. As can be seen, the content words reflecting the discussion topics (such as ‘‘supreme court’’ and ‘‘gun rights’’) ap- pear in context of the discourse flow, where participants carry the conversation forward via making a statement, giving …

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