In this paper, we show that the combination of Phrase Pair Injection and
Corpus Filtering boosts the performance of Neural Machine Translation (NMT)
systems. We extract parallel phrases and sentences from the pseudo-parallel
corpus and augment it with the parallel corpus to train the NMT models. With
the proposed approach, we observe an improvement in the Machine Translation
(MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi,
and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on
the FLORES test data. These BLEU score improvements are over the models trained
using the whole pseudo-parallel corpus augmented with the parallel corpus.

本论文表明，短语注入和语料库过滤的组合提高了神经机器翻译 (NMT) 系统的性能。我们从伪平行语料库中提取并增广平行短语和句子，以此训练 NMT 模型。在 Hindi-Marathi、English-Marathi 和 English-Pashto 这 3 种低资源语言对和 6 种翻译方向上，我们观察到了 NMT 系统的改进，并在 FLORES 测试数据上提高了 2.7 BLEU 分，这些 BLEU 分数改进是基于使用整个伪平行语料库和平行语料库增广的模型。