多语言有毒文本净化：用词典引导+分类器过滤，提升跨语言净化效果。
ylmmcl at Multilingual Text Detoxification 2025: Lexicon-Guided Detoxification and Classifier-Gated Rewriting
- 基于多语言毒害词典进行精准标记，结合微调的序列生成模型。
- 在开发集和测试集上平均J分数达0.612，STA达0.922，表现最优。
- 适用于多语言场景，尤其在英语、俄语、法语等高资源语言中泛化能力强。
本文介绍ylmmcl团队在PAN-2025多语言文本净化任务中的解决方案：一个融合词典引导标记、微调的序列到序列模型（s-nlp/mt0-xl-detox-orpo）以及迭代式分类器门控机制的鲁棒多语言净化流水线。该方法通过多语言毒害词典实现显式毒性词汇标注，显著提升了净化精度与跨语言泛化能力。最终模型在自评中达到最高STA值0.922，并在开发集与测试集上实现平均官方J分数0.612，xCOMET得分分别为0.793（开发集）和0.787（测试集）。相较基线与回译方法，该模型在多种语言中表现更优，尤其在英语、俄语、法语等高资源语言中展现出强泛化性。尽管在相似度（SIM）方面存在一定权衡，但净化强度持续提升。竞赛中，团队以0.612的得分位列第九。
原文摘要 · Abstract (English)
In this work, we introduce our solution for the Multilingual Text Detoxification Task in the PAN-2025 competition for the ylmmcl team: a robust multilingual text detoxification pipeline that integrates lexicon-guided tagging, a fine-tuned sequence-to-sequence model (s-nlp/mt0-xl-detox-orpo) and an iterative classifier-based gatekeeping mechanism. Our approach departs from prior unsupervised or monolingual pipelines by leveraging explicit toxic word annotation via the multilingual_toxic_lexicon to guide detoxification with greater precision and cross-lingual generalization. Our final model achieves the highest STA (0.922) from our previous attempts, and an average official J score of 0.612 for toxic inputs in both the development and test sets. It also achieved xCOMET scores of 0.793 (dev) and 0.787 (test). This performance outperforms baseline and backtranslation methods across multiple languages, and shows strong generalization in high-resource settings (English, Russian, French). Despite some trade-offs in SIM, the model demonstrates consistent improvements in detoxification strength. In the competition, our team achieved ninth place with a score of 0.612.
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