A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection
2026-04-09 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors worked on detecting sarcasm in Chinese social media posts, which is hard because existing datasets are small and mostly focus only on the text. They created a new larger dataset by using a computer model called a GAN and GPT-3.5 to generate more sarcastic comments, including information about the users' past writing styles. Then, they improved a popular language model (BERT) to use these user patterns, helping the model better spot sarcasm. Their method performed better than previous ones, showing that considering users' language habits helps sarcasm detection.
sarcasm detectionChinese NLPGenerative Adversarial Network (GAN)Large Language Model (LLM)data augmentationBERTSina Weibouser linguistic patternsGPT-3.5F1-score
Authors
Wenxian Wang, Xiaohu Luo, Junfeng Hao, Xiaoming Gu, Xingshu Chen, Zhu Wang, Haizhou Wang
Abstract
Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony, or comparison. Existing methods for Chinese sarcasm detection are constrained by limited datasets and high construction costs, and they mainly focus on textual features, overlooking user-specific linguistic patterns that shape how opinions and emotions are expressed. This paper proposes a Generative Adversarial Network (GAN) and Large Language Model (LLM)-driven data augmentation framework to dynamically model users' linguistic patterns for enhanced Chinese sarcasm detection. First, we collect raw data from various topics on Sina Weibo. Then, we train a GAN on these data and apply a GPT-3.5 based data augmentation technique to synthesize an extended sarcastic comment dataset, named SinaSarc. This dataset contains target comments, contextual information, and user historical behavior. Finally, we extend the BERT architecture to incorporate multi-dimensional information, particularly user historical behavior, enabling the model to capture dynamic linguistic patterns and uncover implicit sarcastic cues in comments. Experimental results demonstrate the effectiveness of our proposed method. Specifically, our model achieves the highest F1-scores on both the non-sarcastic and sarcastic categories, with values of 0.9138 and 0.9151 respectively, which outperforms all existing state-of-the-art (SOTA) approaches. This study presents a novel framework for dynamically modeling users' long-term linguistic patterns in Chinese sarcasm detection, contributing to both dataset construction and methodological advancement in this field.