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

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A Correction to this article was published on 10 July 2021

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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.

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Acknowledgements

The authors would like to appreciate the editors and the reviewers for their constructive suggestions. And the creation of the paper cannot leave with the excellent papers on deep learning and their codes.

This work is supported by the Science and Technology Plan of Yunnan Province of China under grants 2014AB016.

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  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Mingxuan Hu & Min He

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  1. Mingxuan Hu
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  2. Min He
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Correspondence to Min He.

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The original online version of this article was revised: there are errors in some of the equations and data in table 3.

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Hu, M., He, M. Non-parallel text style transfer with domain adaptation and an attention model. Appl Intell 51, 4609–4622 (2021). https://doi.org/10.1007/s10489-020-02077-5

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