Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_Survey
翻译:文本样式传输是自然语言生成中的一项重要任务,目的是控制生成文本中的某些属性,如礼貌、情感、幽默和其他许多属性。它在自然语言处理领域有着悠久的历史,最近由于深层神经模型带来的有希望的性能而重新引起极大关注。本文对神经文本样式传输研究进行系统调查,自2017年第一次神经文本风格传输工作以来,该研究涉及100多条具有代表性的文章。我们讨论了任务配置、现有数据集和子任务、评估,以及存在平行和非平行数据时的丰富方法。我们还提供了有关今后开发这项任务的各种重要议题的讨论。我们整理的文件列表在https://github.com/zjiing-jin/Text_Style_Transfer_Suvey。