Event-Aware Sarcasm Detection in Chinese Social Media Using Multi-Head Attention and Contrastive Learning

Abstract

Sarcasm detection is a complex and challenging task, particularly in the context of Chinese social media, where it exhibits strong contextual dependencies and cultural specificity. To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions, this paper proposes an event-aware model for Chinese sarcasm detection, leveraging a multi-head attention (MHA) mechanism and contrastive learning (CL) strategies. The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers (BERT) encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two, thereby capturing multidimensional semantic associations. Additionally, a CL strategy is introduced to enhance feature representation capabilities, further improving the model’s performance in handling class imbalance and complex contextual scenarios. The model achieves state-of-the-art performance on the Chinese sarcasm dataset, with significant improvements in accuracy (79.55%), F1-score (84.22%), and an area under the curve (AUC, 84.35%).