  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.
