论文标题
文本样式转移：评论和实验评估
Text Style Transfer: A Review and Experimental Evaluation
论文作者
论文摘要
近年来，文本的风格特性吸引了计算语言学研究人员。具体来说，研究人员研究了文本样式转移（TST）任务，该任务旨在在保留其样式独立内容的同时更改文本的风格特性。在过去的几年中，已经开发了许多新颖的TST算法，而该行业利用这些算法来实现令人兴奋的TST应用程序。由于这种共生，TST研究领域迅速发展。本文旨在全面审查有关文本样式转移的最新研究工作。更具体地说，我们创建了一种分类法来组织TST模型，并提供有关最新技术状况的全面摘要。我们回顾了针对TST任务的现有评估方法，并进行了一项大规模的可重复性研究，在其中实验基准了两个公开可用数据集的最先进的TST TST TST算法。最后，我们扩展了当前趋势，并就TST领域的新开发发展提供了新的观点。
The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.
