多语言训练让写作风格模型跨语言更准，提升泛化能力。
Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains
- 用概率掩码让模型关注风格词而非内容词
- 跨语言批量处理减少干扰，提升对比学习效果
- 在21种非英语语言上平均召回率提升4.85%
作者风格表示（AR）学习通过建模作者独特写作风格，在作者归属任务中表现优异。但以往研究主要集中于单语场景（尤其是英语），多语言AR模型的潜力未被充分探索。本文提出一种新型多语言AR学习方法，包含两项关键创新：概率内容掩码，促使模型聚焦风格指示性词汇而非内容特异性词汇；语言感知批处理，通过减少跨语言干扰优化对比学习。模型在超过450万作者、36种语言和13个领域上进行训练。在22种非英语语言中的21种上，性能持续优于单语基线，平均召回率@8提升4.85%，单语言最高提升达15.91%。此外，相比仅用英语训练的单语模型，该模型展现出更强的跨语言与跨领域泛化能力。分析证实两项技术均有效，对性能提升至关重要。
原文摘要 · Abstract (English)
Authorship representation (AR) learning, which models an author's unique writing style, has demonstrated strong performance in authorship attribution tasks. However, prior research has primarily focused on monolingual settings-mostly in English-leaving the potential benefits of multilingual AR models underexplored. We introduce a novel method for multilingual AR learning that incorporates two key innovations: probabilistic content masking, which encourages the model to focus on stylistically indicative words rather than content-specific words, and language-aware batching, which improves contrastive learning by reducing cross-lingual interference. Our model is trained on over 4.5 million authors across 36 languages and 13 domains. It consistently outperforms monolingual baselines in 21 out of 22 non-English languages, achieving an average Recall@8 improvement of 4.85%, with a maximum gain of 15.91% in a single language. Furthermore, it exhibits stronger cross-lingual and cross-domain generalization compared to a monolingual model trained solely on English. Our analysis confirms the effectiveness of both proposed techniques, highlighting their critical roles in the model's improved performance.
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