ARXIV:2604.08381 · SARCASM DETECTION · SUBMITTED 10 APR · 17:36 UTC · FRESHNESS STALE

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

Wenxian Wang · Xiaohu Luo · Junfeng Hao · Xiaoming Gu · Xingshu Chen · Zhu Wang · +1 at arXiv

A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

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Opportunity summary

Pain A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

Evidence 0 refs | 3 sources | 50% coverage

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PROBLEM

A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results. Existing methods for Chinese sarcasm detection are constrained by limited datasets and high construction costs,…

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experimental results demonstrate the effectiveness of our proposed method. Code availability is flagged in the production record; the public repository link still needs proof…

WHY NOW

Sarcasm Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

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Opportunity summary

Score7.0

PainA GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

Evidence0 refs | 3 sources | 50% coverage

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Analysis summary

A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

Segment

Sarcasm Detection

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A GAN and LLM-driven framework that dynamically models user linguistic patterns for enhanced Chinese sarcasm detection, achieving state-of-the-art results.

Segment

Sarcasm Detection

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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

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

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