Non-parallel text style transfer is an important task in natural language
generation. However, previous studies concentrate on the token or sentence
level, such as sentence sentiment and formality transfer, but neglect long
style transfer at the discourse level. Long texts usually involve more
complicated author linguistic preferences such as discourse structures than
sentences. In this paper, we formulate the task of non-parallel story
author-style transfer, which requires transferring an input story into a
specified author style while maintaining source semantics. To tackle this
problem, we propose a generation model, named StoryTrans, which leverages
discourse representations to capture source content information and transfer
them to target styles with learnable style embeddings. We use an additional
training objective to disentangle stylistic features from the learned discourse
representation to prevent the model from degenerating to an auto-encoder.
Moreover, to enhance content preservation, we design a mask-and-fill framework
to explicitly fuse style-specific keywords of source texts into generation.
Furthermore, we constructed new datasets for this task in Chinese and English,
respectively. Extensive experiments show that our model outperforms strong
baselines in overall performance of style transfer and content preservation.

本文提出了一个名为 StoryTrans 的生成模型，该模型利用话语表示来捕捉源内容信息，并将其与可学习的样式嵌入一起传输到目标样式以解决非并行故事作者样式转换问题，并且使用一个附加的训练目标来解开样式特征，防止模型退化为一个自动编码器，并构建了新的汉英两种语言数据集，实验证明我们的模型在样式转换和内容保护的整体性能方面优于强基线。