A Hierarchical Reinforced Sequence Operation Method forUnsupervised Text Style Transfer
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 lowlevel 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