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).1Extensive 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 .
Highlights
The last decade has witnessed the revolution of communication, where the “kitchen table conversations" have been expanded to public discussions on online platforms
To distinguish the above two components, we examine the conversation contexts and identify two types of words: topic words, indicating what a conversation focuses on, and discourse words, reflecting how the opinion is voiced in each message
We first report the topic coherence results in Section 5.1, followed by a discussion in Section 5.2 comparing the latent discourse roles discovered by our model with the manually annotated dialogue acts
Summary
The last decade has witnessed the revolution of communication, where the “kitchen table conversations" have been expanded to public discussions on online platforms. The flourish of microblogs has led to the sheer quantity of usercreated conversations emerging every day, exposing individuals to superfluous information. The content words reflecting the discussion topics (such as “supreme court” and “gun rights”) appear in context of the discourse flow, where participants carry the conversation forward via making a statement, giving a comment, asking a question, and so forth. Motivated by such an observation, we assume that a microblog conversation can be decomposed into two crucially different components: one for topical content and the other for discourse behavior.
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