arXiv cs.CL· Omkar Yadav, Michael Jiang, Kenan Alkiek, Jian Zhu, David Jurgens·· 11 小时前AI 评分22
让作者表征捕捉多种写作风格：作者级风格混合模型提升作者归属任务表现
Consistent yet Contextual: Learning Author-Level Mixtures of Style for Authorship Attribution
AI 导读
研究者提出多种作者级建模方案，允许单个作者的表征捕捉多种一致但不同的写作风格，在作者验证与归属任务上取得显著性能提升。基于该模型，研究在写作受众规模变化、与训练时间的时间距离、前文上下文影响、体裁规范影响四个场景下考察作者写作的个人风格程度，发现作者之间的个人化程度差异很大。结果表明作者需以多种风格表征建模，且部分场景产生的文本对判断作者身份更具信息量。
正文 · 原文
Abstract:Approaches to authorship verification or attribution typically model the style of a document, asking whether two documents were likely written by the same author. While recent approaches have been performant, such approaches largely ignore modeling the authors themselves and how varied their style is. Here, we introduce multiple alternatives for modeling the author and demonstrate that allowing an author's representation to capture multiple consistent but distinct writing styles yields significant performance improvements. Using our new authorship model, we then examine when an author writes in a way that is most idiomatic of their style across four scenarios: (i) variation in the size of their writing audiences, (ii) temporal distance from when the model was trained, (iii) the effect of preceding context, and (iv) the effect of genre norms on style. Across all four experiments, we demonstrate that authors vary substantially in how idiolectic their writing is. Our results have clear implications for both the modeling and data used for authorship verification and attribution, showing that authors need to be represented with their multiple styles and that some settings produce text that is much more informative about who wrote that text.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.07111 [cs.CL] |
| (or arXiv:2611.07111v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07111 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: David Jurgens [view email]
[v1]
Wed, 4 Nov 2026 20:25:47 UTC (187 KB)
来源：arXiv cs.CL · arxiv.org