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Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation

2020-04-06 11:50:49
Wei Peng, Chongxuan Huang, Tianhao Li, Yun Chen, Qun Liu
arXiv_CL
     
Abstract

Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. These methods suffer from issues associated with a domain information gap, which leads to translation errors for low frequency and out-of-vocabulary terminology. This paper proposes a dictionary-based data augmentation (DDA) method for cross-domain NMT. DDA synthesizes a domain-specific dictionary with general domain corpora to automatically generate a large-scale pseudo-IND parallel corpus. The generated pseudo-IND data can be used to enhance a general domain trained baseline. The experiments show that the DDA-enhanced NMT models demonstrate consistent significant improvements, outperforming the baseline models by 3.75-11.53 BLEU. The proposed method is also able to further improve the performance of the back-translation based and IND-finetuned NMT models. The improvement is associated with the enhanced domain coverage produced by DDA.

Abstract (translated)

URL

https://arxiv.org/abs/2004.02577

PDF

https://arxiv.org/pdf/2004.02577.pdf

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