Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable. To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a low-level agent that alters the sentence. We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents. Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.
@article{arxiv.1906.01833,
title = {A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer},
author = {Chen Wu and Xuancheng Ren and Fuli Luo and Xu Sun},
journal= {arXiv preprint arXiv:1906.01833},
year = {2019}
}