This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.

本文提出了一种新的礼貌转化任务，旨在将非礼貌语句转化为礼貌性语句并维持原意。我们提供了一个包含1.39多个自动标记有礼貌度的数据集，以鼓励对这一新任务进行基准评估。我们设计了一个标记生成管道，识别出风格属性，然后生成目标风格的句子，同时保留了源内容的大部分信息。对于礼貌性以及其他五个转移任务，我们的模型在内容保留的自动度量标准上优于现有技术，同时在风格转换准确性方面具有可比或更好的表现。此外，我们的模型在语法正确性、意义保留和转移准确性的人类评估方面优于现有方法的六个风格转移任务的所有。数据和代码位于此https URL。

礼貌转换：标注与生成方法