论文标题
文本样式转移的深度学习：调查
Deep Learning for Text Style Transfer: A Survey
论文作者
论文摘要
文本样式转移是自然语言产生的重要任务，该任务旨在控制生成的文本中的某些属性，例如礼貌，情感，幽默和许多其他属性。它在自然语言处理领域具有悠久的历史，并且由于深度神经模型带来了有希望的表现，最近重新获得了重大关注。在本文中，我们介绍了对神经文本样式转移研究的系统调查，涵盖自2017年首次神经文本样式转移工作以来的100多种代表性文章。我们讨论了在存在并行和非平行数据的情况下，我们讨论了任务公式，现有数据集和子任务，评估以及丰富的方法论。我们还提供了有关此任务未来发展的各种重要主题的讨论。我们精心策划的纸张清单在https://github.com/zhijing-jin/text_style_transfer_survey上
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
