June 5, 2024, 4:52 a.m. | Jiashu Yao, Heyan Huang, Zeming Liu, Yuhang Guo

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.02517v1 Announce Type: new
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 …

abstract arxiv augmentation augmented data consistent cs.cl data diverse generate machine machine translation neural machine translation operations random sampling semantic translation type

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