Text Style Transfer Back-Translation

• URL: http://arxiv.org/abs/2306.01318v1
• Date: Fri, 2 Jun 2023 07:33:47 GMT
• Title: Text Style Transfer Back-Translation
• Authors: Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
• Abstract summary: Back Translation improves translation of inputs that share a similar style. For natural inputs, BT brings only slight improvements and sometimes even adverse effects. We propose Text Style Transfer Back Translation, which uses a style transfer model to modify the source side of BT data.
• Score: 14.608570096595177
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs that share a similar style (to be more specific, translation-like inputs), since the source side of BT data is machine-translated. For natural inputs, BT brings only slight improvements and sometimes even adverse effects. To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data. By making the style of source-side text more natural, we aim to improve the translation of natural inputs. Our experiments on various language pairs, including both high-resource and low-resource ones, demonstrate that TST BT significantly improves translation performance against popular BT benchmarks. In addition, TST BT is proved to be effective in domain adaptation so this strategy can be regarded as a general data augmentation method. Our training code and text style transfer model are open-sourced.

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