Fluency Matters! Controllable Style Transfer with Syntax Guidance (2023)

Abstract: Unsupervised text style transfer is a challenging task that aims to alter the stylistic attributes of a given text without affecting its original content. One of the methods to achieve this is controllable style transfer, which allows for the control of the degree of style transfer. However, an issue encountered with controllable style transfer is the instability of transferred text fluency when the degree of the style transfer changes. To address this problem, we propose a novel approach that incorporates additional syntax parsing information during style transfer. By leveraging the syntactic information, our model is guided to generate natural sentences that effectively reflect the desired style while maintaining fluency. Experimental results show that our method achieves robust performance and improved fluency compared to previous controllable style transfer methods.

## proposed method we formulate the syntax-guided text style transfer as follows: given an input text x, its corresponding style label s, and syntax parse p as model inputs, we train our model using an autoencoder to reconstruct x while preserving the style s. training the model based on reconstruction is necessary in an unsupervised setting due to the lack of a parallel dataset. the actual style transfer takes place during inference time. ## conclusion in this paper, we proposed a controllable, syntaxguided text style transfer model. we improved the fluency of transferred sentences, irrespective of the style transfer strength, by incorporating syntax parses and concatenating their embeddings with semantic and style embeddings. our approach outperformed previous controllable models on two datasets in terms of consistent ppl scores and natural sentence generation while preserving context. however, our model yielded lower bleu scores compared to other controllable style transfer models. future work aims to improve content preservation capabilities while maintaining performance across varying style transfer weights.