论文标题
跨域神经机器翻译的基于字典的数据增强
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation
论文作者
论文摘要
现有的神经机器翻译（NMT）的数据增强方法主要依赖于反向翻译内域（IND）单语库中。这些方法遇到了与域信息差距相关的问题，这会导致低频和频率范围内术语的翻译错误。本文提出了一种基于词典的数据增强（DDA）方法，用于跨域NMT。 DDA将特定于域的字典与通用域Corpora合成，以自动生成一个大规模的伪指标平行语料库。生成的伪指标数据可用于增强一般域训练的基线。实验表明，DDA增强的NMT模型表现出一致的显着改进，表现优于3.75-11.53 BLEU。所提出的方法还能够进一步提高基于反向翻译和基于IND的NMT模型的性能。改进与DDA产生的增强域覆盖率有关。
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.
