Dialogue systems have attracted growing research interests due to its widespread applications in various domains. Existing studies on classification tasks in dialogue systems majorly focus on the sentence-level intent recognition of users’ utterances. While in real-world applications, classification of the entire dialogue, also benefits many downstream tasks such as customer satisfaction analysis, service quality assurance, dialogue topic categorization, etc. In this paper, we propose DialGNN, a heterogeneous graph neural network framework tailored for the problem of dialogue classification which takes the entire dialogue as input. Specifically, a heterogeneous graph is constructed with nodes in different levels of semantic granularity. The graph framework allows flexible integration of various pre-trained language representation models, such as BERT and its variants, which endows DialGNN with powerful text representational capabilities. The experimental results on two real-world datasets demonstrate the robustness and the effectiveness of the proposed DialGNN framework. The implementation of DialGNN and related data are shared through https://github.com/anonymous-auth/ DialGNN.
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