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分离开来是计算语言学中一个长期存在且可能无法完全解决的问题。与此同时，近年来带有作者标签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用于作者归因的作者表示——该任务在理论上更依赖于对写作风格的编码而非内容编码。然而，在这一替代任务上的成功并不能保证此类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。为了更深入地理解这些表示所传达信息的本质，并具体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性地探查了这些表示。实验结果表明，为替代性作者预测任务学习到的表示确实对写作风格敏感。因此，作者表示可能对某些类型的数据漂移（如随时间推移的主题漂移）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）开辟道路。