|本期目录/Table of Contents|

[1]杨郑鑫,李京谕,胡镓伟,等.基于增量训练的维汉神经机器翻译系统[J].厦门大学学报(自然科学版),2019,58(02):195-199.[doi:10.6043/j.issn.0438-0479.201811019]
 YANG Zhengxin,LI Jingyu,HU Jiawei,et al.Uyghur-to-Chinese neural machine translation based on incremental training[J].Journal of Xiamen University(Natural Science),2019,58(02):195-199.[doi:10.6043/j.issn.0438-0479.201811019]
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基于增量训练的维汉神经机器翻译系统(PDF/HTML)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
58卷
期数:
2019年02期
页码:
195-199
栏目:
民族语言处理
出版日期:
2019-03-27

文章信息/Info

Title:
Uyghur-to-Chinese neural machine translation based on incremental training
文章编号:
0438-0479(2019)02-0195-05
作者:
杨郑鑫12李京谕12胡镓伟12冯 洋12*
1.中国科学院计算技术研究所,智能信息处理重点实验室,北京 100190; 2.中国科学院大学计算机科学与技术学院,北京 100049
Author(s):
YANG Zhengxin12LI Jingyu12HU Jiawei12 FENG Yang12*
1.Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Science,Beijing 100190,China; 2.School of Computer Science and Technology,University of Chinese Academy of Science,Beijing 100049,China
关键词:
自然语言处理 神经机器翻译 维吾尔语
Keywords:
natural language processing neural machine translation Uyghur
分类号:
TP 183
DOI:
10.6043/j.issn.0438-0479.201811019
文献标志码:
A
摘要:
目前,基于深度学习的神经机器翻译已经成为机器翻译领域的主流方法.神经机器翻译模型相较于统计机器翻译模型具有更庞大的参数规模,因此其翻译质量取决于训练数据是否充足.由于与维吾尔语相关的平行语料资源严重匮乏,神经机器翻译模型在维汉翻译任务上表现不佳,为此提出了一种利用伪语料对神经机器翻译模型进行增量训练的方法,可有效提升神经机器翻译在维汉翻译任务上的质量.
Abstract:
At present,the neural machine translation based on deep learning has become the mainstream method in the field of machine translation.The neural machine translation model requires a larger parameter size than the statistical machine translation model does. Therefore, its translation quality depends on the sufficiency of the training data.Due to the serious lack of parallel corpus resources related to Uyghur,the neural machine translation model performs poorly on Uyghur-to-Chinese translation tasks.This paper proposes a method of incremental training of neural machine translation models using pseudo-corpus,which effectively improves the quality of neural machine translation in Uyghur-to-Chinese translation tasks.

参考文献/References:

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[13] MELAMED I D,GREEN R,TURIAN J P.Precision and recall of machine translation[C]∥Proceedings of HLT-NAACL 2003.Edmonton:Association for Computational Linguistics,2003:61-63.
[14] KLAKOW D,PETERS J.Testing the correlation of word error rate and perplexity[J].Speech Communication,2002,38(1/2):19-28.
[15] LEUSCH G,UEFFING N,NEY H.A novel string-to-string distance measure with applications to machine translation evaluation[C]∥Proceedings of MT Summit Ⅸ 2003.New Orleans:[s.n.],2003:240-247.
[16] 刘群,刘洋.一种机器翻译自动评测方法及其系统:中国,ZL200410000628.8[P].2009-10-28.
[17] BANERJEE S,LAVIE A.METEOR:an automatic metric for MT evaluation with improved correlation with human judgments[C]∥Proceedings of ACL 2005.Ann Arbor:Association for Computational Linguistics,2005:65-72.
[18] SNOVER M,DORR B,SCHWARTZ R,et al.A study of translation edit rate with targeted human annotation[C]∥Proceedings of AMTA 2006.Cambridge:Association for Machine Translation in the Americas,2006:223-231.

备注/Memo

备注/Memo:
收稿日期:2018-11-11 录用日期:2018-12-03
基金项目:国家自然科学基金(61662077)
*通信作者:fengyang@ict.ac.cn
更新日期/Last Update: 1900-01-01