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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2023: Advances in Knowledge Discovery and Data Mining pp 210–221Cite as

Unsupervised Text Style Transfer Through Differentiable Back Translation and Rewards

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13938))

Abstract

In this paper, we propose an end-to-end system for unsupervised text style transfer (UTST). Prior studies on UTST work on the principle of disentanglement between style and content features, which successfully accomplishes the task of generating style-transferred text. The success of a style transfer system depends on three criteria, viz. Style transfer accuracy, Content preservation of source, and Fluency of the generated text. Generated text by disentanglement-based method achieves better style transfer performance but suffers from the lack of content preservation as the previous works suggest. To develop an all-around solution to all three aspects, we use a reinforcement learning-based training objective that gives rewards to the model for generating fluent style transferred text while preserving the source content. On the modeling aspect, we develop a shared encoder and style-specific decoder architecture which uses the Transformer architecture as a backbone. This modeling choice enables us to frame a differentiable back translation objective aiding better content preservation as shown through a careful ablation study. We conclude this paper with both automatic and human evaluation, showing the superiority of our proposed method on sentiment and formality style transfer tasks. Code is available at https://github.com/newcodevelop/Unsupervised-TST.

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Notes

  1. 1.

    https://huggingface.co/docs/transformers/model_doc/distilbert.

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Acknowledgement

The research reported in this paper is an outcome of the project “HELIOS-Hate, Hyperpartisan, and Hyperpluralism Elicitation and Observer System", sponsored by Wipro.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, Patna, India

    Dibyanayan Bandyopadhyay & Asif Ekbal

Authors
  1. Dibyanayan Bandyopadhyay
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  2. Asif Ekbal
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Correspondence to Dibyanayan Bandyopadhyay .

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Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Hisashi Kashima

  2. IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Tsuyoshi Ide

  3. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

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Bandyopadhyay, D., Ekbal, A. (2023). Unsupervised Text Style Transfer Through Differentiable Back Translation and Rewards. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_17

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  • DOI: https://doi.org/10.1007/978-3-031-33383-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33382-8

  • Online ISBN: 978-3-031-33383-5

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