Text style transfer techniques are gaining popularity in natural language
processing allowing paraphrasing text in the required form: from toxic to
neural, from formal to informal, from old to the modern English language, etc.
Solving the task is not sufficient to generate some neural/informal/modern
text, but it is important to preserve the original content unchanged. This
requirement becomes even more critical in some applications such as style
transfer of goal-oriented dialogues where the factual information shall be kept
to preserve the original message, e.g. ordering a certain type of pizza to a
certain address at a certain time. The aspect of content preservation is
critical for real-world applications of style transfer studies, but it has
received little attention. To bridge this gap we perform a comparison of
various style transfer models on the example of the formality transfer domain.
To perform a study of the content preservation abilities of various style
transfer methods we create a parallel dataset of formal vs. informal
task-oriented dialogues. The key difference between our dataset and the
existing ones like GYAFC [17] is the presence of goal-oriented dialogues with
predefined semantic slots essential to be kept during paraphrasing, e.g. named
entities. This additional annotation allowed us to conduct a precise
comparative study of several state-of-the-art techniques for style transfer.
Another result of our study is a modification of the unsupervised method LEWIS
[19] which yields a substantial improvement over the original method and all
evaluated baselines on the proposed task.

文本风格转换方法在自然语言处理中越来越受欢迎，允许将文本从有毒到神经、从正式到非正式、从古老到现代英语等形式进行改写。在一些应用中，解决这一任务不仅仅是生成一些神经 / 非正式 / 现代化的文本，更重要的是保持原始内容不变。通过比较各种风格转换模型在正式转换领域的实例，我们对各种风格转换方法的内容保留能力进行了研究。我们创建了一个形式与非正式任务导向的对话的平行数据集，与 GYAFC 等现有数据集的关键区别是包含了必须在改写过程中保留的预定义语义槽，例如命名实体。这个额外的注释允许我们对几种最先进的风格转换技术进行精确的对比研究。我们研究的另一个结果是对无监督方法 LEWIS 进行改进，在所提出的任务中取得了明显的改进，优于原始方法和所有评估的基准方法。