Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains

• URL: http://arxiv.org/abs/2509.16531v1
• Date: Sat, 20 Sep 2025 04:43:24 GMT
• Title: Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains
• Authors: Junghwan Kim, Haotian Zhang, David Jurgens,
• Abstract summary: Authorship representation (AR) learning has demonstrated strong performance in authorship attribution tasks.<n>We introduce a novel method for multilingual AR learning that incorporates two key innovations.<n>Our model is trained on over 4.5 million authors across 36 languages and 13 domains.
• Score: 41.44674318564781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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