用风格化数据增强提升新闻主观性识别准确率
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation
- 通过迁移学习专用编码器，比通用微调更有效
- 精心设计的增广使模型在主观内容检测上表现显著提升
- 适合关注文本情感与立场分析的研究者
本文提交至 CLEF 2025 检查真相实验室任务1——主观性检测。研究了迁移学习与风格化数据增强在英文新闻文本主观/客观句分类中的效果。对比了预训练编码器微调与在相关任务上迁移微调的Transformer模型性能。引入基于 GPT-4o 的可控增广流程，生成具有指定主观风格的改写句子，并使用同一模型对生成样本进行修正以保证标签与风格一致性。结果显示，特定编码器的迁移学习优于通用微调模型，且精心策划的增广显著提升模型鲁棒性，尤其在主观内容识别上。官方提交排名24人中第16位。研究结果表明，结合编码器专业化与标签一致的增广策略，能有效提升主观性检测性能。代码已开源：https://github.com/dsgt-arc/checkthat-2025-subject。
原文摘要 · Abstract (English)
This paper presents our submission to Task 1, Subjectivity Detection, of the CheckThat! Lab at CLEF 2025. We investigate the effectiveness of transfer-learning and stylistic data augmentation to improve classification of subjective and objective sentences in English news text. Our approach contrasts fine-tuning of pre-trained encoders and transfer-learning of fine-tuned transformer on related tasks. We also introduce a controlled augmentation pipeline using GPT-4o to generate paraphrases in predefined subjectivity styles. To ensure label and style consistency, we employ the same model to correct and refine the generated samples. Results show that transfer-learning of specified encoders outperforms fine-tuning general-purpose ones, and that carefully curated augmentation significantly enhances model robustness, especially in detecting subjective content. Our official submission placed us $16^{th}$ of 24 participants. Overall, our findings underscore the value of combining encoder specialization with label-consistent augmentation for improved subjectivity detection. Our code is available at https://github.com/dsgt-arc/checkthat-2025-subject.
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