Contrastive Learning for Authorship Verification
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Computer Science > Computation and Language
Title:Contrastive Learning for Authorship Verification
Abstract:Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.
| Comments: | Published in the proceedings of CLEF 2026. Code: this https URL |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.28471 [cs.CL] |
| (or arXiv:2609.28471v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.28471
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2026), LNCS 17087, pp. 92-102, Springer (2027) |
| Related DOI: | https://doi.org/10.1007/978-3-032-39150-6_7
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