Past approaches to translate a phrase in a language L1 to a language L2 using
a dictionary-based approach require grammar rules to restructure initial
translations. This paper introduces a novel method without using any grammar
rules to translate a given phrase in L1, which does not exist in the
dictionary, to L2. We require at least one L1-L2 bilingual dictionary and
n-gram data in L2. The average manual evaluation score of our translations is
4.29/5.00, which implies very high quality.

本文介绍了一种新的翻译方法，使用至少一个 L1-L2 的双语词典和 L2 的 n-gram 数据来翻译 L1 中给定的短语，无需使用任何语法规则，平均手动评估得分为 4.29/5.00，品质非常高。

使用双语词典和 n-gram 数据的短语翻译：以越南文到英文为例的实证研究

Phrase translation using a bilingual dictionary and n-gram data: A case study from Vietnamese to English

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.

本文提出了一种基于词典的数据增强方法，用于跨领域神经机器翻译。通过生成大规模的伪 IND 平行语料库，可以增强一般域训练的基线 NMT 模型，实验结果表明，DDA 增强的 NMT 模型表现出一致显著的改进，胜过基线模型 3.75-11.53 BLEU。