S tory T rans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing (2023)

Abstract: 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.

## introduction text style transfer aims to endow a text with a different style while keeping its main semantic content unaltered. it has a wide range of applications, such as formality transfer @xcite , sentiment transfer @xcite and author-style imitation @xcite . due to the lack of parallel corpora, recent works mainly focus on unsupervised transfer by selfreconstruction. current methods proposed to dis-guo han was a talented man in ancient times. one summer evening, he was enjoying the cool in the courtyard. suddenly, a gust of wind brought a refreshing fragrance, and a young girl descended from the sky on a white cloud and appeared in front of guo han … 郭翰在夏日的夜,院中花香沁人心脾,一阵清香从身旁飘来,那少女 却是神色自若,一言不发的从天而降。郭翰大惊,眼前白光一闪,身 前纱衣一晃,已被她夺了过去。… on a summer night, the scent of flowers is refreshing in the courtyard，a scent of fragrance floats from guo han side. a young girl, with a calm expression, fell from the sky without saying a word. guo han was shocked, and already taken away by her with a white light flashing and the gauze flickering. … source text: generated text for jy style: in this paper, we present the first study on story author-style transfer, which aims to rewrite a story incorporating source content and the target author style. the first challenge of this task lies in imitation of author's linguistic choices at the discourse level, such as narrative techniques (e.g., brief or detailed writing). as exemplified in to alleviate the above issues, we propose a generation framework, named storytrans, which learns discourse representations from source texts and then combines these representations with learnable style embeddings to generate texts of target styles. furthermore, we propose a new training objective to reduce stylistic features from the discourse representations, which aims to pull the representations derived from different texts close in the latent space. to enhance content preservation, we separate the generation process into two stages, which first transfers the source text with the style-specific content keywords masked and then generates the whole text by imposing these keywords explicitly. to support the evaluation of the proposed task, we collect new datasets in chinese and english based on existing story corpora. 2 we conduct extensive experiments to transfer fairy tales (in chinese) or everyday stories (in english) to typical author styles, respectively. automatic evaluation results show that our model achieves a better overall performance in style control and content preservation than strong baselines. the manual evaluation also confirms the efficacy of our model. we summarize the key contributions of this work as follows: i. to the best of our knowledge, we present the first study on story author style transfer. we construct new chinese and english datasets for this task. ## conclusion in this paper, we present the first study for story author-style transfer and analyze the difficulties of this task. accordingly, we propose a novel generation model, which explicitly disentangles the style information from high-level text representations to improve the style transfer accuracy, and achieve better content preservation by injecting style-specific contents. automatic evaluations show storytrans outperform baselines on the overall performance. further analysis shows storytrans has a better ability to capture linguistic features for style transfer.