稀缺资源语言神经网络机器翻译研究综述

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. Low-resource NMT has been one of the most popular issues in MT and attracted wide attention around the world in recent years. This paper presents a survey on low-resource NMT research. We first introduce some related academic activities and feasible data sets for the translation, then categorize and summarize several types of approaches mainly used in low-resource NMT in detail, and present their features, as well as relations between them, and describe the current research status. Finally, we propose some advices on possible research trends and directions of this field in the future.