Replacing Language Model for Style Transfer

We introduce replacing language model (RLM), a sequence-to-sequence language modeling framework for text style transfer. Our method autoregressively replaces each token in the original sentence with a text span in the target style. In contrast, the new span is generated via a non-autoregressive masked language model. The RLM generation scheme gathers the flexibility of autoregressive models and the accuracy of non-autoregressive models, which bridges the gap between sentence-level and word-level style transfer methods. To further control the style of generated sentences, we conduct a style-content disentanglement on the hidden representations of RLM. Empirical results on real-world text style transfer tasks demonstrate the effectiveness of RLM compared with other baselines.