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.

本文提出了一个无监督框架，用于共同建模微博交谈中的主题内容和话语行为。具体而言，我们提出了一个神经模型来发现表示会话涉及什么（即主题）和反映参与者表达意见的词群（即话语）。广泛的实验表明，我们的模型既能产生连贯的主题又能反映有意义的话语行为。进一步的研究表明，我们的主题和话语表征可以增强微博信息分类的性能，特别是在分类器联合训练的情况下。