It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, underperforming phrase-based
statistical machine translation (PBSMT) and requiring large amounts of
auxiliary data to achieve competitive results. In this paper, we re-assess the
validity of these results, arguing that they are the result of lack of system
adaptation to low-resource settings. We discuss some pitfalls to be aware of
when training low-resource NMT systems, and recent techniques that have shown
to be especially helpful in low-resource settings, resulting in a set of best
practices for low-resource NMT. In our experiments on German--English with
different amounts of IWSLT14 training data, we show that, without the use of
any auxiliary monolingual or multilingual data, an optimized NMT system can
outperform PBSMT with far less data than previously claimed. We also apply
these techniques to a low-resource Korean-English dataset, surpassing
previously reported results by 4 BLEU.

本文探讨神经网络机器翻译（NMT）在低资源条件下性能下降的原因，提出适应低资源环境时的注意事项和最佳实践，并在德语 - 英语和韩语 - 英语低资源翻译数据集上进行实验，发现经过优化的 NMT 系统可以在没有使用其他语言辅助数据的情况下，比以前报告的更少数据超越采用词组统计的基于规则的机器翻译（PBSMT），BLEU 指标超过 4 个点。