Computer Science ›› 2022, Vol. 49 ›› Issue (1): 271-278.doi: 10.11896/jsjkx.201200094

• Artificial Intelligence • Previous Articles     Next Articles

Unsupervised Domain Adaptation Based on Style Aware

NING Qiu-yi, SHI Xiao-jing, DUAN Xiang-yu, ZHANG Min   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2020-12-09 Revised:2021-03-21 Online:2022-01-15 Published:2022-01-18
  • About author:NING Qiu-yi,born in 1995,postgra-duate.Her main research interests include machine translation and domain adaptation.
    DUAN Xiang-yu,born in 1976,Ph.D,professor.His main research interests include machine translationand cross-language information processing.
  • Supported by:
    National Natural Science Foundation of China(61673289).

Abstract: In recent years,neural machine translation has made significant progress in translation quality,but it relies on parallel bilingual sentence pairs heavily during the training process.However,parallel resources are scarce for the e-commerce domain,in addition,cultural differences lead to stylistic differences in product information expression.To solve these two problems,a style-aware unsupervised domain adaptation algorithm is proposed,which makes full use of e-commerce monolingual data in the mutual training method,while introducing quasi knowledge distillation approach to deal with style differences.We construct non-parallel bilingual corpus by obtaining e-commerce product data information,and then carry out experiments based on the aforementioned corpus and Chinese and English news parallel corpus.The results show that the algorithm significantly improves translation qua-lity compared to various unsupervised domain adaptation methods,improves about 5 BLEU points compared with the strongest baseline system.In addition,the algorithm is further extended to Ted,Law and Medical OPUS data,all of which achieve better translation results.

Key words: Domain adaptation, E-commerce, Machine translation, Style aware, Unsupervised

CLC Number: 

  • TP183
[1]CURREY A,BARONE A V M,HEAFIELD K.Copied Monolingual Data Improves Low-Resource Neural Machine Translation[C]//Proceedings of the Second Conference on Machine Translation.Denmark:Association for Computational Linguistics,2017:148-156.
[2]SENNRICH R,HADDOW B,BIRCH A.Improving Neural Machine Translation Models with Monolingual Data[C]//Procee-dings of the 54th Annual Meeting of the Association for Computational Linguistics.Germany:Association for Computational Linguistics,2016:86-96.
[3]DOU Z Y,HU J J,ANASTASOPOULOS A,et al.Unsuper-vised domain adaptation for neural machine translation with domain-aware feature embeddings[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Proces-sing and the 9th International Joint Conference on Natural Language Processing.Hong Kong:Association for Computational Linguistics,2019:1417-1422.
[4]HU J,XIA M,NEUBIG G,et al.Domain Adaptation of Neural Machine Translation by Lexicon Induction[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics.Italy:Association for Computational Linguistics,2019:2989-3001.
[5]SHEN Y,LEONARD D,PAVEL P,et al.Word-based Domain Adaptation for Neural Machine Translation[C]//Proceedings of the International Workshop on Spoken Language Translation.Belgium,2019.
[6]ZHANG Z,LIU S,LI M,et al.Joint Training for Neural Machine Translation Models with Monolingual Data[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.USA:AAAI Press,2018:555-562.
[7]HE D,XIA Y,QIN T,et al.Dual Learning for Machine Translation[J].Advances in Neural Information Processing Systems 29:Annual Conference on Neural Information Processing Systems,2016(12):820-828.
[8]ZHEN Y,WEI C,FENG W,et al.Unsupervised Domain Adaptation for Neural Machine Translation[C]//24th International Conference on Pattern Recognition.China:IEEE Computer So-ciety,2018:338-343.
[9]ZHENG Z,ZHOU H,HUANG S,et al.Mirror-generative neural machine translation[C]//8th International Conference on Learning Representations.Ethiopia:ICLR,2020.
[10]NIU X,RAO S,CARPUAT M.Multi-task neural models fortranslating between styles within and across languages[C]//Proceedings of the 27th International Conference on Computational Linguistics.USA:Association for Computational Linguistics,2018:1008-1020.
[11]KOEHN P,OCH F J,MARCU D.Statistical Phrase-BasedTranslation[C]//Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics.Canada:Association for Computational Linguistics,2013:48-54.
[12]ARTETXE M,LABAKA G,AGIRRE E.An Effective Ap-proach to Unsupervised Machine Translation[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics.Italy:Association for Computational Linguistics,2019:194-203.
[13]ARTETXE M,LABAKA G,AGIRRE E.Unsupervised Statisti-cal Machine Translation[C]//Proceedings of the 2018 Confe-rence on Empirical Methods in Natural Language Processing.Belgium:Association for Computational Linguistics,2018:3623-3642.
[14]LUONG T,PHAM H,MANNING C D.Effective Approaches to Attention-based Neural Machine Translation[C]//Procee-dings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon.Portugal:Association for Computational Linguistics,2015:1412-1421.
[15]ASHISH V,NOAM S,NIKI P,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems 30.USA:NIPS,2017:5998-6008.
[16]YOON K,ALEXANDER M R.Sequence-Level Knowledge Distillation[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.USA:Association for Computational Linguistics,2016:1317-1327.
[17]SUTSKEVER I,ORIOL V,QUOC V L.Sequence to sequence learning with neural networks[C]//Advances in Neural Information Processing Systems 27.Canada:NIPS,2014:3104-3112.
[18]SENNRICH R,HADDOW B,BIRCH A.Neural MachineTranslation of Rare Words with Subword Units[C]//Procee-dings of the 54th Annual Meeting of the Association for Computational Linguistics.Germany:Association for Computational Linguistics,2016:1715-1725.
[19]PAPINENI K,ROUKOS S,WARD T,et al.Bleu:a method for automatic evaluation of machine translation [C]//Proceedings of the 40th annual meeting of the Association for Computational Linguistics.USA:Association for Computational Linguistics,2002:311-318.
[20]CHRIS D,VICTOR C,NOAH A S.A Simple,Fast,and Effective Reparameterization of IBM Model 2[C]//Proceedings of the North American Chapter of the Association for Computational Linguistics.USA:Association for Computational Linguistics,2013:644-648.
[21]HU D M,ZHU C G,HU C,et al.Multilingual Text EmotionalAnalysis with Pre-trained Model and Attention Mechanism[J].Journal of Chinese Mini-Micro Computer Systems,2020,41(2):278-284.
[22]QIAO B W,LI J H.Neural Machine Translation CombiningSource Semantic Roles[J].Computer Science,2020,47(2):163-168.
[1] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2] LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96.
[3] DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[4] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[5] XU Ming-yue. Study on Information Sharing and Channel Strategy of Platform in Consideration ofInformation Leakage and Information Investing Cost [J]. Computer Science, 2022, 49(6A): 744-752.
[6] DONG Zhen-heng, REN Wei-ping, YOU Xin-dong, LYU Xue-qiang. Machine Translation Method Integrating New Energy Terminology Knowledge [J]. Computer Science, 2022, 49(6): 305-312.
[7] LIU Kai, ZHANG Hong-jun, CHEN Fei-qiong. Name Entity Recognition for Military Based on Domain Adaptive Embedding [J]. Computer Science, 2022, 49(1): 292-297.
[8] LIU Jun-peng, SU Jin-song, HUANG De-gen. Incorporating Language-specific Adapter into Multilingual Neural Machine Translation [J]. Computer Science, 2022, 49(1): 17-23.
[9] YU Dong, XIE Wan-ying, GU Shu-hao, FENG Yang. Similarity-based Curriculum Learning for Multilingual Neural Machine Translation [J]. Computer Science, 2022, 49(1): 24-30.
[10] HOU Hong-xu, SUN Shuo, WU Nier. Survey of Mongolian-Chinese Neural Machine Translation [J]. Computer Science, 2022, 49(1): 31-40.
[11] LIU Yan, XIONG De-yi. Construction Method of Parallel Corpus for Minority Language Machine Translation [J]. Computer Science, 2022, 49(1): 41-46.
[12] LIU Chuang, XIONG De-yi. Survey of Multilingual Question Answering [J]. Computer Science, 2022, 49(1): 65-72.
[13] WU Lan, WANG Han, LI Bin-quan. Unsupervised Domain Adaptive Method Based on Optimal Selection of Self-supervised Tasks [J]. Computer Science, 2021, 48(6A): 357-363.
[14] LIU Rong, ZHANG Ning. Application Status and Future Trends of Photo Analysis in E-commerce:A Survey of Research Based on Photo Visual and Content Features [J]. Computer Science, 2021, 48(6A): 137-142.
[15] LIU Xiao-die. Recognition and Transformation for Complex Noun Phrases Based on Boundary Perception [J]. Computer Science, 2021, 48(6A): 299-305.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!