The difficulty of textual style transfer lies in the lack of parallel
corpora. Numerous advances have been proposed for the unsupervised generation.
However, significant problems remain with the auto-evaluation of style transfer
tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019),
style transfer evaluations rely on three criteria: style accuracy of
transferred sentences, content similarity between original and transferred
sentences, and fluency of transferred sentences. We elucidate the problematic
current state of style transfer research. Given that current tasks do not
represent real use cases of style transfer, current auto-evaluation approach is
flawed. This discussion aims to bring researchers to think about the future of
style transfer and style transfer evaluation research.

本文讨论了文本风格迁移技术中的关键问题，即使用无监督生成方法，自动评估风格迁移任务的难点。通过对类似文献的总结，我们阐述了当前风格迁移研究的问题，并指出现有的自动评估方法存在缺陷，无法准确评估迁移后句子的风格准确性、内容相似度和流畅性。本文旨在引导研究者思考风格迁移和评估研究的未来趋势。

现实世界文本风格转移自动评估的艰巨任务

The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation

In this paper we discuss a very simple approach of combining content and link
information in graph structures for the purpose of community discovery, a
fundamental task in network analysis. Our approach hinges on the basic
intuition that many networks contain noise in the link structure and that
content information can help strengthen the community signal. This enables ones
to eliminate the impact of noise (false positives and false negatives), which
is particularly prevalent in online social networks and Web-scale information
networks.
Specifically we introduce a measure of signal strength between two nodes in
the network by fusing their link strength with content similarity. Link
strength is estimated based on whether the link is likely (with high
probability) to reside within a community. Content similarity is estimated
through cosine similarity or Jaccard coefficient. We discuss a simple mechanism
for fusing content and link similarity. We then present a biased edge sampling
procedure which retains edges that are locally relevant for each graph node.
The resulting backbone graph can be clustered using standard community
discovery algorithms such as Metis and Markov clustering.
Through extensive experiments on multiple real-world datasets (Flickr,
Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate
the effectiveness and efficiency of our methods over state-of-the-art learning
and mining approaches several of which also attempt to combine link and content
analysis for the purposes of community discovery. Specifically we always find a
qualitative benefit when combining content with link analysis. Additionally our
biased graph sampling approach realizes a quantitative benefit in that it is
typically several orders of magnitude faster than competing approaches.

本文介绍了一种简单的方法，即将内容和链接信息结合起来构建图结构用于社区发现，通过在网络节点之间融合链接强度与内容相似度来评估信号强度，提出了一种有效的去噪方法并采用偏倚边采样，使用 Metis 和 Markov 聚类等算法对骨干图进行聚类，多次实验显示此方法效果显著且速度较快。