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.

自动从作者的写作风格中脱离其内容是计算语言学中一个长期存在且可能难以逾越的问题。然而，最近可用的带有作者标签的大型文本语料库使得可以通过完全基于数据驱动的方式来学习作者表征，用于作者归属的任务，这个任务似乎更多地依赖于编码写作风格而不是编码内容。然而，成功完成这个替代任务并不能确保这些表征能够捕捉到写作风格，因为作者归属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征传达的信息性质，特别是验证它们主要是否编码写作风格的假设，我们通过一系列有针对性的实验系统地探查了这些表征。这些实验的结果表明，学习用于替代作者归属预测任务的表征确实对写作风格敏感。因此，可预期作者表征对某些数据转换具有稳健性，例如随时间的主题漂移。此外，我们的发现可能为需要风格表征的下游应用开启大门，如风格转换。

作者身份表达学习能捕捉到文体特征吗？

Can Authorship Representation Learning Capture Stylistic Features?

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an
ideal dialogue generation models should be able to capture the topic
information of each context, detect the relevant context, and produce
appropriate responses accordingly. However, existing models usually use word or
sentence level similarities to detect the relevant contexts, which fail to well
capture the topical level relevance. In this paper, we propose a new model,
named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is
pre-trained on the whole training dataset. Then, the topic level attention
weights are computed based on the topic representation of each context.
Finally, the attention weights and the topic distribution are utilized in the
decoding process to generate the corresponding responses. Experimental results
on both Chinese customer services data and English Ubuntu dialogue data show
that STAR-BTM significantly outperforms several state-of-the-art methods, in
terms of both metric-based and human evaluations.

本文提出了一种基于 Biterm Topic Model（BTM）的对话生成模型 STAR-BTM 来解决多轮对话中话题漂移的问题，并在中文客服数据和英文 Ubuntu 对话数据上进行了实验，结果表明 STAR-BTM 在度量和人工评估方面明显优于现有模型。