Text style transfer is the task of transferring the style of text having
certain stylistic attributes, while preserving non-stylistic or content
information. In this work we introduce the Generative Style Transformer (GST) -
a new approach to rewriting sentences to a target style in the absence of
parallel style corpora. GST leverages the power of both, large unsupervised
pre-trained language models as well as the Transformer. GST is a part of a
larger `Delete Retrieve Generate' framework, in which we also propose a novel
method of deleting style attributes from the source sentence by exploiting the
inner workings of the Transformer. Our models outperform state-of-art systems
across 5 datasets on sentiment, gender and political slant transfer. We also
propose the use of the GLEU metric as an automatic metric of evaluation of
style transfer, which we found to compare better with human ratings than the
predominantly used BLEU score.

本文提出了一种新的手段 —— 生成式转换器（GST），其使用无监督的预训练语言模型和 Transformer，以重写句子以达到目标风格的目的，并使用 GLEU 评估度量，超出其他当今最先进的情感、性别和政治审查数据集上的性能。

用于受控文本风格转换的删除、检索、生成方法转换

Transforming Delete, Retrieve, Generate Approach for Controlled Text  Style Transfer

The GLEU metric was proposed for evaluating grammatical error corrections
using n-gram overlap with a set of reference sentences, as opposed to
precision/recall of specific annotated errors (Napoles et al., 2015). This
paper describes improvements made to the GLEU metric that address problems that
arise when using an increasing number of reference sets. Unlike the originally
presented metric, the modified metric does not require tuning. We recommend
that this version be used instead of the original version.

本文描述了对 GLEU 度量标准的改进，解决了使用越来越多参考集合时出现的问题，并且不需要调整参数，建议使用改进后的指标。