区分风格与内容：通过数据选择与呈现增强跨体裁作者归属识别
Separating Style from Substance: Enhancing Cross-Genre Authorship Attribution through Data Selection and Presentation
AI总结：
针对跨主题与跨体裁作者归属识别中训练数据稀缺及模型依赖主题的问题，本文提出训练数据选择方法和新型学习课程，显著提升跨体裁识别性能。
摘要：
判断两份文档是否由同一作者撰写，对机器和人类而言都是一项具有挑战性的任务。当两份文档涉及不同主题（例如棒球与政治）或采用不同体裁（例如博客文章与学术论文）时，这一任务会更加困难。对机器而言，现实世界中跨越主题边界的训练样本相对缺乏，而跨体裁数据更是极为稀缺，这使问题更加复杂。我们提出了用于训练数据选择的针对性方法，以及一种新颖的学习课程，旨在抑制模型在作者归属识别中对主题信息的依赖，并相应地迫使模型纳入无论主题如何都能更稳健地指示风格的信息。这些改进使平均跨体裁作者归属识别性能相对提升62.7%，并在单一体裁条件下提升16.6%。
英文摘要：
The task of deciding whether two documents are written by the same author is challenging for both machines and humans. This task is even more challenging when the two documents are written about different topics (e.g. baseball vs. politics) or in different genres (e.g. a blog post vs. an academic article). For machines, the problem is complicated by the relative lack of real-world training examples that cross the topic boundary and the vanishing scarcity of cross-genre data. We propose targeted methods for training data selection and a novel learning curriculum that are designed to discourage a model's reliance on topic information for authorship attribution and correspondingly force it to incorporate information more robustly indicative of style no matter the topic. These refinements yield a 62.7% relative improvement in average cross-genre authorship attribution, as well as 16.6% in the per-genre condition.
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。
