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This paper presents an unsupervised framework for jointly modeling topiccontent and discourse behavior in microblog conversations. Concretely, wepropose a neural model to discover word clusters indicating what a conversationconcerns (i.e., topics) and those reflecting how participants voice theiropinions (i.e., discourse). Extensive experiments show that our model can yieldboth coherent topics and meaningful discourse behavior. Further study showsthat our topic and discourse representations can benefit the classification ofmicroblog messages, especially when they are jointly trained with theclassifier.

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