A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection

2026-04-09 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

A new Generative Adversarial Network (GAN) and Large Language Model (LLM)-driven data augmentation framework has been developed to improve Chinese sarcasm detection. This framework addresses limitations in existing methods, such as small datasets and a focus solely on textual features, by incorporating user-specific linguistic patterns. Researchers collected raw data from Sina Weibo, then used a GAN and a GPT-3.5-based technique to create an extended sarcastic comment dataset called SinaSarc. This dataset includes target comments, contextual information, and user historical behavior. The BERT architecture was extended to integrate this multi-dimensional information, particularly user historical behavior, allowing the model to capture dynamic linguistic patterns and implicit sarcastic cues. The proposed method achieved F1-scores of 0.9138 for non-sarcastic and 0.9151 for sarcastic categories, outperforming all previous approaches.

Key takeaway

For research scientists developing NLP models for nuanced language tasks like sarcasm detection, you should consider integrating multi-dimensional user behavior data. This approach, demonstrated by extending BERT with user historical patterns, can significantly improve model performance beyond text-only analysis, especially in low-resource languages. Explore GAN and LLM-driven data augmentation to expand your training datasets and capture dynamic linguistic patterns.

Key insights

Combining GANs and LLMs for data augmentation significantly enhances Chinese sarcasm detection by modeling user-specific linguistic patterns.

Principles

• User historical behavior reveals implicit sarcastic cues.
• Data augmentation can overcome dataset limitations.

Method

Train a GAN on raw social media data, then use a GPT-3.5-based technique to augment the dataset with user historical behavior and contextual information, and finally extend BERT to incorporate these multi-dimensional features.

In practice

• Augment datasets with GANs and LLMs.
• Integrate user historical data into NLP models.

Topics

• Chinese Sarcasm Detection
• Data Augmentation
• Generative Adversarial Networks
• Large Language Models
• BERT Architecture

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.