Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.

自 2000 年代初以来，神经机器翻译 (NMT) 在过去十年中取得了巨大的增长，并已进入成熟阶段。虽然被认为是机器翻译使用最广泛的解决方案，但由于大型并行语料库的不可用性，与高资源语言对相比，它在低资源语言对上的性能仍然不是最佳的。因此，针对低资源语言对的 NMT 技术的实施最近受到了关注，从而引发了对该主题的大量研究。本文详细调查了低资源语言 NMT (LRL-NMT) 的研究进展，并进行了定量分析以确定最流行的技术。我们根据我们的发现为给定的 LRL 数据设置提供了选择可能的 NMT 技术的指南。