Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer

TL;DR

This paper introduces a semi-supervised text style transfer framework that combines bootstrapping with reinforcement learning, achieving state-of-the-art results with minimal labeled data.

Contribution

It proposes a novel semi-supervised approach that leverages pseudo-parallel data and stepwise reward optimization to improve style transfer performance.

Findings

Achieves state-of-the-art results on multiple datasets.

Effective with as little as 10% of training data.

Stabilizes reinforcement learning with stepwise rewards.

Abstract

Text style transfer is an important task in controllable language generation. Supervised approaches have pushed performance improvement on style-oriented rewriting such as formality conversion. However, challenges remain due to the scarcity of large-scale parallel data in many domains. While unsupervised approaches do not rely on annotated sentence pairs for each style, they are often plagued with instability issues such as mode collapse or quality degradation. To take advantage of both supervised and unsupervised paradigms and tackle the challenges, in this work, we propose a semi-supervised framework for text style transfer. First, the learning process is bootstrapped with supervision guided by automatically constructed pseudo-parallel pairs using lexical and semantic-based methods. Then the model learns from unlabeled data via reinforcement rewards. Specifically, we propose to improve the sequence-to-sequence policy gradient via stepwise reward optimization, providing fine-grained learning signals and stabilizing the reinforced learning process. Experimental results show that the proposed approach achieves state-of-the-art performance on multiple datasets, and produces effective generation with as minimal as 10\% of training data.