用合成数据训练更去内容化的风格嵌入，提升文本风格识别效果。
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
- 用大模型生成带可控风格变化的近似改写句对，构建对比学习数据集
- 在40种风格特征上实现精准对比学习，显著降低内容干扰
- 适用于需要剥离内容影响的风格分析任务，如文本风格迁移
风格表示旨在将相似写作风格的文本映射得更接近，不同风格的则更远，且与内容无关。然而，传统对比三元组常同时包含风格和内容差异，导致表示中出现内容泄露。本文提出StyleDistance，通过大语言模型生成具有受控风格变化的近似重述句对，构建涵盖40种风格特征的正负样本数据集，实现精确对比学习。通过人工与自动评估验证了合成数据与嵌入质量。该方法显著提升了风格嵌入的内容独立性，在真实世界基准上表现优异，优于现有主流风格表示方法。模型可于https://huggingface.co/StyleDistance/styledistance 获取。
原文摘要 · Abstract (English)
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representations may vary in both style and content, leading to potential content leakage in the representations. We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings. We use a large language model to create a synthetic dataset of near-exact paraphrases with controlled style variations, and produce positive and negative examples across 40 distinct style features for precise contrastive learning. We assess the quality of our synthetic data and embeddings through human and automatic evaluations. StyleDistance enhances the content-independence of style embeddings, which generalize to real-world benchmarks and outperform leading style representations in downstream applications. Our model can be found at https://huggingface.co/StyleDistance/styledistance .
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