A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer

Chen Wu, Xuancheng Ren, Fuli Luo, Xu Sun

August 2019

Our proposed Point-Then-Operate (PTO) applied to a real test sample. A high-level agent (red squares) iteratively proposes operation positions, and a low-level agent (arrows) alters the sentence based on the high-level proposals. Compared with seq2seq methods, PTO is more interpretable and better preserves style-independent contents.

Abstract

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.

Type

Conference paper

Publication

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, Volume 1: Long Papers