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        <title>Deterministic Reversible Data Augmentation for Neural Machine Translation</title>
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    <abstract>Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data because of irreversible operations and random subword sampling procedures. To generate both symbolically diverse and semantically consistent augmentation data, we propose Deterministic Reversible Data Augmentation (DRDA), a simple but effective data augmentation method for neural machine translation. DRDA adopts deterministic segmentations and reversible operations to generate multi-granularity subword representations and pulls them closer together with multi-view techniques. With no extra corpora or model changes required, DRDA outperforms strong baselines on several translation tasks with a clear margin (up to 4.3 BLEU gain over Transformer) and exhibits good robustness in noisy, low-resource, and cross-domain datasets.</abstract>
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%0 Conference Proceedings
%T Deterministic Reversible Data Augmentation for Neural Machine Translation
%A Yao, Jiashu
%A Huang, Heyan
%A Liu, Zeming
%A Guo, Yuhang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yao-etal-2024-deterministic
%X Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data because of irreversible operations and random subword sampling procedures. To generate both symbolically diverse and semantically consistent augmentation data, we propose Deterministic Reversible Data Augmentation (DRDA), a simple but effective data augmentation method for neural machine translation. DRDA adopts deterministic segmentations and reversible operations to generate multi-granularity subword representations and pulls them closer together with multi-view techniques. With no extra corpora or model changes required, DRDA outperforms strong baselines on several translation tasks with a clear margin (up to 4.3 BLEU gain over Transformer) and exhibits good robustness in noisy, low-resource, and cross-domain datasets.
%U https://aclanthology.org/2024.findings-acl.481
%P 8075-8089

Markdown (Informal)

[Deterministic Reversible Data Augmentation for Neural Machine Translation](https://aclanthology.org/2024.findings-acl.481) (Yao et al., Findings 2024)

• Deterministic Reversible Data Augmentation for Neural Machine Translation (Yao et al., Findings 2024)

ACL

• Jiashu Yao, Heyan Huang, Zeming Liu, and Yuhang Guo. 2024. Deterministic Reversible Data Augmentation for Neural Machine Translation. In Findings of the Association for Computational Linguistics ACL 2024, pages 8075–8089, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.