Despite impressive empirical successes of neural machine translation (NMT) on
standard benchmarks, limited parallel data impedes the application of NMT
models to many language pairs. Data augmentation methods such as
back-translation make it possible to use monolingual data to help alleviate
these issues, but back-translation itself fails in extreme low-resource
scenarios, especially for syntactically divergent languages. In this paper, we
propose a simple yet effective solution, whereby target-language sentences are
re-ordered to match the order of the source and used as an additional source of
training-time supervision. Experiments with simulated low-resource
Japanese-to-English, and real low-resource Uyghur-to-English scenarios find
significant improvements over other semi-supervised alternatives.

本文提出一种简单但有效的方法，即将目标语句重新排序以匹配源语序，并将其作为另外一种训练时的监督信号，从而在模拟低资源日语 - 英语和真实低资源维吾尔 - 英语语种中获得显着改进。

低资源机器翻译中的语法差异处理

Handling Syntactic Divergence in Low-resource Machine Translation

Neural machine translation (NMT) has achieved notable performance recently.
However, this approach has not been widely applied to the translation task
between Chinese and Uyghur, partly due to the limited parallel data resource
and the large proportion of rare words caused by the agglutinative nature of
Uyghur. In this paper, we collect ~200,000 sentence pairs and show that with
this middle-scale database, an attention-based NMT can perform very well on
Chinese-Uyghur/Uyghur-Chinese translation. To tackle rare words, we propose a
novel memory structure to assist the NMT inference. Our experiments
demonstrated that the memory-augmented NMT (M-NMT) outperforms both the vanilla
NMT and the phrase-based statistical machine translation (SMT). Interestingly,
the memory structure provides an elegant way for dealing with words that are
out of vocabulary.

本文介绍了使用中等规模的数据集和新颖的记忆结构在汉维翻译中应用基于注意力机制的神经机器翻译，并且此方法可解决极少见词汇的问题。研究发现，记忆增强型 NMT 处理稀缺词汇的效果优于传统 NMT 和基于短语的统计机器翻译。