|Table of Contents|

Uyghur-to-Chinese neural machine translation based on incremental training(PDF)

Journal of Xiamen University(Natural Science)[ISSN:0438-0479/CN:35-1070/N]

Issue:
2019 02
Page:
195-199
Research Field:
National Language Processing
Publishing date:
2019-03-27

Info

Title:
Uyghur-to-Chinese neural machine translation based on incremental training
Article ID:
0438-0479(2019)02-0195-05
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
Keywords:
natural language processing neural machine translation Uyghur
CLC number:
TP 183
DOI:
10.6043/j.issn.0438-0479.201811019
Document code:

A
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.

References:

[1] CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]∥Proceedings of EMNLP 2014.Baltimore:Association for Computational Linguistics,2014:1724-1734.
[2] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[C]∥Proceedings of ICLR 2015.San Diego:International Conference on Learning Representations,2015:1409.0473.
[3] GEHRING J,AULI M,GRANGIER D,et al.Convolutional sequence to sequence learning[C]∥Proceedings of ICML 2017.Sydney:International Conference on Machine Learning,2017:1705.03122.
[4] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]∥Proceedings of NIPS 2017.Long Beach:Conference on Neural Information Processing Systems,2017:1706.03762.
[5] SENNRICH R,HADDOW B,BIRCH A.Improving neural machine translation models with monolingual data[C]∥Proceedings of ACL 2016.Berlin:Association for Computational Linguistics,2016:86-96.
[6] LUONG M,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[C]∥Proceedings of EMNLP 2015.Lisbon:Association for Computational Linguistics,2015:1412-1421.
[7] WU Y,SCHUSTER M,CHEN Z,et al.Google’s neural machine translation system:bridging the gap between human and machine translation[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1609.08144.
[8] SUTSKEVER I,VINYALS V,LE Q V.2014.Sequence to sequence learning with neural networks[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1409.3215.
[9] SENNRICH R,HADDOW B,BIRCH A.Neural machine translation of rare words with subword units[C]∥Proceedings of ACL 2016.Berlin:Association for Computational Linguistics,2016:1715-1725.
[10] JOHNSON M,SCHUSTER M,LE Q V,et al.2016 Google’s multilingual neural machine translation system:enabling zero-shot translation[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1611.04558.
[11] CHIANG D,DENEEFE S,CHAN Y S,et al.Decomposability of translation metrics for improved evaluation and efficient algorithms[EB/OL].[2018-11-27].https:∥www3.nd.edu/~dchiang/papers/bleu.pdf.
[12] DODDINGTON G.Automatic evaluation of machine translation quality using N-gram co-occurrence statistics[EB/OL].[2018-11-27].http:∥www.mt-archive.info/HLT-2002-Doddington.pdf.
[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.[1] CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]∥Proceedings of EMNLP 2014.Baltimore:Association for Computational Linguistics,2014:1724-1734.
[2] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[C]∥Proceedings of ICLR 2015.San Diego:International Conference on Learning Representations,2015:1409.0473.
[3] GEHRING J,AULI M,GRANGIER D,et al.Convolutional sequence to sequence learning[C]∥Proceedings of ICML 2017.Sydney:International Conference on Machine Learning,2017:1705.03122.
[4] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]∥Proceedings of NIPS 2017.Long Beach:Conference on Neural Information Processing Systems,2017:1706.03762.
[5] SENNRICH R,HADDOW B,BIRCH A.Improving neural machine translation models with monolingual data[C]∥Proceedings of ACL 2016.Berlin:Association for Computational Linguistics,2016:86-96.
[6] LUONG M,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[C]∥Proceedings of EMNLP 2015.Lisbon:Association for Computational Linguistics,2015:1412-1421.
[7] WU Y,SCHUSTER M,CHEN Z,et al.Google’s neural machine translation system:bridging the gap between human and machine translation[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1609.08144.
[8] SUTSKEVER I,VINYALS V,LE Q V.2014.Sequence to sequence learning with neural networks[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1409.3215.
[9] SENNRICH R,HADDOW B,BIRCH A.Neural machine translation of rare words with subword units[C]∥Proceedings of ACL 2016.Berlin:Association for Computational Linguistics,2016:1715-1725.
[10] JOHNSON M,SCHUSTER M,LE Q V,et al.2016 Google’s multilingual neural machine translation system:enabling zero-shot translation[EB/OL].[2018-11-27].https:∥arxiv.org/pdf/1611.04558.
[11] CHIANG D,DENEEFE S,CHAN Y S,et al.Decomposability of translation metrics for improved evaluation and efficient algorithms[EB/OL].[2018-11-27].https:∥www3.nd.edu/~dchiang/papers/bleu.pdf.
[12] DODDINGTON G.Automatic evaluation of machine translation quality using N-gram co-occurrence statistics[EB/OL].[2018-11-27].http:∥www.mt-archive.info/HLT-2002-Doddington.pdf.
[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.

Memo

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
Last Update: 1900-01-01