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). 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.
Authors: Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael Lyu, Irwin King (The Chinese University of Hong Kong, Tencent AI Lab, University of Warwick)