Neural machine translation (NMT) has achieved impressive performance on
machine translation task in recent years. However, in consideration of
efficiency, a limited-size vocabulary that only contains the top-N highest
frequency words are employed for model training, which leads to many rare and
unknown words. It is rather difficult when translating from the low-resource
and morphologically-rich agglutinative languages, which have complex morphology
and large vocabulary. In this paper, we propose a morphological word
segmentation method on the source-side for NMT that incorporates morphology
knowledge to preserve the linguistic and semantic information in the word
structure while reducing the vocabulary size at training time. It can be
utilized as a preprocessing tool to segment the words in agglutinative
languages for other natural language processing (NLP) tasks. Experimental
results show that our morphologically motivated word segmentation method is
better suitable for the NMT model, which achieves significant improvements on
Turkish-English and Uyghur-Chinese machine translation tasks on account of
reducing data sparseness and language complexity.

本研究提出了一种基于形态学的单词分割方法，能够在训练时缩小词汇表的同时保留词结构中的语言和语义信息，以协助神经机器翻译，实验结果表明此方法能显著提高土耳其 - 英语和维吾尔 - 中文机器翻译任务的性能，降低数据稀疏性和语言复杂性。