Text Style Transfer Back-Translation
Daimeng Wei Zhanglin Wu Hengchao Shang Zongyao Li Minghan Wang Jiaxin Guo Xiaoyu Chen Zhengzhe Yu Hao Yang
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.