Non-parallel text style transfer with domain adaptation and an attention model

Abstract

Text style transfer, the aim of which is to convert a specific style in a given sentence to another target style while maintaining the style-independent content information of the original sentence, can face challenges when applied to non-parallel text. In this paper, we combine domain adaptation learning and an attention model to propose a new framework to accomplish the task. Domain adaptation can leverage relative information from the source domain to improve the generative model’s capacity for reconstructing data. The attention model can give the importance weights of generated words for the target style in a sentence; therefore, the generative model can concentrate on generating words with higher importance weights to accomplish text style transfer effectively. We evaluate our framework using Yelp, Amazon and Captions corpora. The results of automatic and human evaluation demonstrate the effectiveness of our framework compared with previous works under non-parallel and limited training data. The available codes are in https://github.com/mingxuan007/text-style-transfer-with-adversarial-network-and-domain-adaptation .