DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification

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.