Authorship has entangled style and content inside. Authors frequently write about the same topics in the same style, so when different authors write about the exact same topic the easiest way out to distinguish them is by understanding the nuances of their style. Modern neural models for authorship can pick up these features using contrastive learning, however, some amount of content leakage is always present. Our aim is to reduce the inevitable impact and correlation between content and authorship. We present a technique to use contrastive learning (InfoNCE) with additional hard negatives synthetically created using a semantic similarity model. This disentanglement technique aims to distance the content embedding space from the style embedding space, leading to embeddings more informed by style. We demonstrate the performance with ablations on two different datasets and compare them on out-of-domain challenges. Improvements are clearly shown on challenging evaluations on prolific authors with up to a 10% increase in accuracy when the settings are particularly hard. Trials on challenges also demonstrate the preservation of zero-shot capabilities of this method as fine tuning.

该研究针对作者身份与内容间的纠缠问题，通过创新的方法减少内容泄漏对作者身份识别的影响。作者提出了一种结合对比学习与语义相似性模型生成的硬负样本的技术，从而实现内容嵌入和风格嵌入空间的分离，显著提高了作者身份识别的准确性，尤其在挑战性评估中提升了多达10%的准确率。

通过语义嵌入和对比学习分离作者身份与内容