Automatically disentangling an author's style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics. At the same time, the availability of large text corpora furnished with author labels has recently enabled learning authorship representations in a purely data-driven manner for authorship attribution, a task that ostensibly depends to a greater extent on encoding writing style than encoding content. However, success on this surrogate task does not ensure that such representations capture writing style since authorship could also be correlated with other latent variables, such as topic. In an effort to better understand the nature of the information these representations convey, and specifically to validate the hypothesis that they chiefly encode writing style, we systematically probe these representations through a series of targeted experiments. The results of these experiments suggest that representations learned for the surrogate authorship prediction task are indeed sensitive to writing style. As a consequence, authorship representations may be expected to be robust to certain kinds of data shift, such as topic drift over time. Additionally, our findings may open the door to downstream applications that require stylistic representations, such as style transfer.

翻译：自动将作者的写作风格与内容解耦，是计算语言学中一个长期存在且可能难以解决的问题。与此同时，近年来带有作者标注的大规模文本语料库的出现，使得我们能够以纯数据驱动的方式学习作者表示以进行作者归属——这项任务本质上更依赖于编码写作风格而非内容。然而，在这项替代任务上的成功并不能确保这些表示捕捉到了写作风格，因为作者身份也可能与其他潜在变量（如主题）相关。为了更好地理解这些表示所传达信息的本质，特别是验证它们主要编码写作风格的假设，我们通过一系列针对性实验系统地探察了这些表示。实验结果表明，为作者预测替代任务学习到的表示对写作风格确实敏感。因此，作者表示可能对某些类型的数据偏移（如随时间变化的主题漂移）具有鲁棒性。此外，我们的发现可能为需要风格表示的下游应用（如风格迁移）打开大门。