Cuiyun Gao


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What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Jichuan Zeng | Jing Li | Yulan He | Cuiyun Gao | Michael R. Lyu | Irwin King
Transactions of the Association for Computational Linguistics, Volume 7

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).1 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.Our data sets and code are available at:


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Topic Memory Networks for Short Text Classification
Jichuan Zeng | Jing Li | Yan Song | Cuiyun Gao | Michael R. Lyu | Irwin King
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.