Hierarchical Information Fusion Graph Neural Networks for Chinese Implicit Rhetorical Questions Recognition

The rhetorical question is a commonly used rhetorical technique in modern Chinese. It can be divided into explicit rhetorical questions and implicit rhetorical questions according to whether it contains rhetorical cues. The implicit rhetorical questions express more emotion and are more complex in form, which has caught the attention of researchers. Most previous works relied too much on manually designed features or massive labeled data, failing to address the integration of multi-level information in sentences. In this paper, we propose a novel implicit rhetorical questions recognition framework named Hierarchical Information Fusion Graph Neural Networks (HIFGN). In particular, in order to improve the robustness of the encoder, we design a contrastive adversarial representation learning approach to map samples into the representation space. Moreover, different subgraphs of the heterogeneous graph are constructed to help the model extract profound sentence information from diverse perspectives. The experimental results on the Chinese rhetorical questions corpus show that the proposed HIFGN model achieves state-of-the-art performance on the task of implicit rhetorical questions recognition.