Non-Parallel Story Author-Style Transfer with Disentangled Representation Learning

Non-parallel story author-style transfer is an important but challenging task in natural language process, which requires transferring an input story into another author-style while maintaining source semantics. Despite recent progress, current text style transfer systems still face the challenges of low robustness of the model and low quality of the generated stories. To address these challenges, we propose an end-to-end framework incorporating dual encoder components and a fusion mechanism, which can achieve explicit style-content disentanglement and effectively fusing source-domain content with target-domain stylistic features. First, we extract text from source stories containing content information using empirical extraction rules and prompt engineering. And then, we propose a novel generation model which achieves story-style transfer through capturing source content features and target style features and then fusing them. We use two additional training objectives to learn high-level discourse representations. Moreover, we have constructed a new dataset for this task. Extensive experiments based on automatic and human evaluation show that our model significantly outperforms state-of-the-art baselines, achieving approximately 8.5% average improvement in comprehensive performance metrics, demonstrating the effectiveness of our model in story-style transfer.