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Non-parallel text style transfer is an important task in natural languagegeneration. However, previous studies concentrate on the token or sentencelevel, such as sentence sentiment and formality transfer, but neglect longstyle transfer at the discourse level. Long texts usually involve morecomplicated author linguistic preferences such as discourse structures thansentences. In this paper, we formulate the task of non-parallel storyauthor-style transfer, which requires transferring an input story into aspecified author style while maintaining source semantics. To tackle thisproblem, we propose a generation model, named StoryTrans, which leveragesdiscourse representations to capture source content information and transferthem to target styles with learnable style embeddings. We use an additionaltraining objective to disentangle stylistic features from the learned discourserepresentation to prevent the model from degenerating to an auto-encoder.Moreover, to enhance content preservation, we design a mask-and-fill frameworkto 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 strongbaselines in overall performance of style transfer and content preservation.

StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing