HALvest-Contrastive: 基于补丁级后期交互的检索式作者归属
HALvest-Contrastive: Retrieval-Like Authorship Attribution with Patch-Level Late Interaction
AI总结：
针对文本作者归属任务中主题混淆问题，提出HALvest多语料库及其对比版本HALvest-Contrastive，并采用补丁级后期交互方法提升跨主题作者识别性能。
摘要：
作者归属任务旨在判断两段文本是否由同一作者所写，但主题混淆使得该任务看似简单：两位作者讨论同一主题可能比一位作者讨论两个主题看起来更相似。学术散文提供了一种自然解决方案：学术作者在保持一致风格习惯的同时，会撰写多篇关于相关但不同主题的论文。我们引入了HALvest，一个包含170亿词元的开放获取学术论文多语料库，及其英语对比衍生版本HALvest-Contrastive，其中同一作者的段落取自学科领域内的不同论文，以最小化主题重叠。我们通过展示一个强词法基线在移除主题捷径后性能崩溃来验证我们的基准。在同一基准上，我们重新审视了作者归属的评分方式。标准系统将每篇文档压缩为单个向量。我们则保留向量序列并通过后期交互进行比较，然后提出补丁级后期交互，在匹配前将相邻词元分组为补丁。序列级匹配相比单向量基线显著提升了性能，但最优交互粒度是微妙的。
英文摘要：
Authorship attribution asks whether two pieces of text share a writer, but topical confound makes the task deceptively easy: two authors covering the same topic may look more alike than one author covering two topics. Scholarly prose offers a natural remedy, academic writers produce multiple papers on related but distinct topics while maintaining consistent stylistic habits. We introduce HALvest, a 17-billion-token multilingual corpus of open-access academic papers, and its English contrastive derivative HALvest-Contrastive, where same-author passages are drawn from distinct papers within a disciplinary field to minimize topical overlap. We validate our benchmark by showing that a strong lexical baseline collapses once topical shortcuts are removed. On this same benchmark, we revisit how authorship is scored. Standard systems compress each document into a single vector. We instead keep a sequence of vectors and compare them with late interaction, then propose patch-level late interaction, which groups neighboring tokens into patches before matching. Matching at the sequence level greatly improves performance over the single-vector baseline, but the optimal interaction granularity is subtle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
