LAUSR

Abstract Text style transfer is an important task in the field of natural language generation. Because of the lack of parallel data, it is a challenge to address this problem in an unsupervised manner. Existing methods mainly focus on the two-style transfer task, i.e. from one source style to one target style. In this paper, we first propose the task of unsupervised text multi-style transfer to address the problem of efficient text transfer from a source style to multiple target styles. To tackle this new task, we present a novel model based on Non-Autoregressive Transformer (NAT). The model consists of two parts: a parameter-shared style-independent module and a style-dependent module. In practice, we only need to reinitialize the parameter of style-dependent modules and retrain the whole model which can converge fast. Experimental results show that our model not only performs well in two-style transfer task, but also promises good results in the multi-style scenario.