CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Exploiting Pre-Ordering for Neural Machine Translation
Zhao, Yang; Zhang, Jiajun; Zong, Chengqing
Conference NameLREC
Conference Date2018-5
Conference PlaceJapan

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation and over-translation problem still remain a big challenge. Through error analysis, we find that under-translation is much more prevalent than over-translation and the source words that need to be reordered during translation are more likely to be ignored. To address the under-translation problem, we explore the pre-ordering approach for NMT. Specifically, we pre-order the source sentences to approximate the target language word order. We then combine the pre-ordering model with position embedding to enhance the monotone translation. Finally, we augment our model with the coverage mechanism to tackle the over-translation problem. Experimental results on Chinese-to-English translation have shown that our method can significantly improve the translation quality by up to 2.43 BLEU points. Furthermore, the detailed analysis demonstrates that our approach can substantially reduce the number of under-translation cases by 30.4% (compared to 17.4% using the coverage model).

Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao, Yang,Zhang, Jiajun,Zong, Chengqing. Exploiting Pre-Ordering for Neural Machine Translation[C],2018.
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