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Position in International Organization

  • Dong Rui  /  无
  • Education: 无
    Direction:  
  • E-mail: dongrui@ms.xjb.ac.cn
    Postal Code: 830011
  • Address: Technical Institute of Physics & Chemistry Chinese Academy of Sciences 40-1 South Beijing Road Urumqi, Xinjiang 830011 China
Resume
 Dong Rui, master supervisor. He received his B.S from Jilin University, and completed his Ph.D. at the Graduate School of the Chinese Academy of Sciences. He is engaged in the research of key technologies of multilingual information processing, and his main research direction is text classification, machine translation, named entity recognition and other tasks related to natural language processing.
 

Research Areas and Achievements： 

Yang mainly engaged in research of multilingual NLP technologies, including machine translation, language identification, topic detection, and content understanding. 

  

Honor： 

Xinjiang Science and Technology Progress Award, first class, 4/12; 

Youth Innovation Promotion Association of Chinese Academy of Sciences; 

Tianshan Excellent Young Scholars of Xinjiang. 

  

Representative Publications： 

[1] Dong Rui, Yang Yating, Jiang Tonghai. Uyghur neural network named entity recognition based on multi linguistic features [J]. Computer Applications and Software, 2020, v.37(05):189-194. 

[2] DONG R, YANG Y, JIANG T. Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation[J]. Information, 2019, 10(6): 202. 

[3] LI Y, LI X, YANG Y, DONG R.A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation[J]. Information, 2020, 11(5): 255. 

[4] PAN Y, LI X, YANG Y, DONG R.Multi-Source Neural Model for Machine Translation of Agglutinative Language[J]. Future Internet, 2020, 12(6): 96. 

[5] ZHANG X, LI X, YANG Y, DONG R.Improving low-resource neural machine translation with teacher-free knowledge distillation[J]. IEEE Access, 2020, 

[6] ZHANG W, LI X, YANG Y, DONG R, LUO G.Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation[J]. Future Internet, 2020, 12(12): 215. 

[7] Yirong Pan, Xiao Li, Yating Yang, Rui Dong. Multi-Task Neural Model for Agglutinative Language Translation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2020: 103-110. 

[8] Pan Yirong, Li Xiao, Yang Yating, Dong Rui. Hierarchical multi-features combination model for Uyghur-Chinese machine translation [J]. Journal of Xiamen University(Natural Science),2020,59(02):206-212. 

[9] Pan Yirong, Li Xiao, Yang Yating, Dong Rui. Bilingual relatedness optimization model for Chinese-Uyghur machine translation [J]. Application Research of Computers,2020,37(03):726-730. 

[10] Zhang xinlu, Li Xiao, Yang Yating, Wang Lei, Dong Rui. Analysis of Bi-directional Reranking Model for Uyghur-Chinese Neural Machine Translation[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, v.56; No.297(01):34-41. 

  

Research Interests: 

Natural language processing. 

Commitment to research the situation
 Dong Rui, master supervisor. He received his B.S from Jilin University, and completed his Ph.D. at the Graduate School of the Chinese Academy of Sciences. He is engaged in the research of key technologies of multilingual information processing, and his main research direction is text classification, machine translation, named entity recognition and other tasks related to natural language processing.
 

Research Areas and Achievements： 

Yang mainly engaged in research of multilingual NLP technologies, including machine translation, language identification, topic detection, and content understanding. 

  

Honor： 

Xinjiang Science and Technology Progress Award, first class, 4/12; 

Youth Innovation Promotion Association of Chinese Academy of Sciences; 

Tianshan Excellent Young Scholars of Xinjiang. 

  

Representative Publications： 

[1] Dong Rui, Yang Yating, Jiang Tonghai. Uyghur neural network named entity recognition based on multi linguistic features [J]. Computer Applications and Software, 2020, v.37(05):189-194. 

[2] DONG R, YANG Y, JIANG T. Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation[J]. Information, 2019, 10(6): 202. 

[3] LI Y, LI X, YANG Y, DONG R.A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation[J]. Information, 2020, 11(5): 255. 

[4] PAN Y, LI X, YANG Y, DONG R.Multi-Source Neural Model for Machine Translation of Agglutinative Language[J]. Future Internet, 2020, 12(6): 96. 

[5] ZHANG X, LI X, YANG Y, DONG R.Improving low-resource neural machine translation with teacher-free knowledge distillation[J]. IEEE Access, 2020, 

[6] ZHANG W, LI X, YANG Y, DONG R, LUO G.Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation[J]. Future Internet, 2020, 12(12): 215. 

[7] Yirong Pan, Xiao Li, Yating Yang, Rui Dong. Multi-Task Neural Model for Agglutinative Language Translation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2020: 103-110. 

[8] Pan Yirong, Li Xiao, Yang Yating, Dong Rui. Hierarchical multi-features combination model for Uyghur-Chinese machine translation [J]. Journal of Xiamen University(Natural Science),2020,59(02):206-212. 

[9] Pan Yirong, Li Xiao, Yang Yating, Dong Rui. Bilingual relatedness optimization model for Chinese-Uyghur machine translation [J]. Application Research of Computers,2020,37(03):726-730. 

[10] Zhang xinlu, Li Xiao, Yang Yating, Wang Lei, Dong Rui. Analysis of Bi-directional Reranking Model for Uyghur-Chinese Neural Machine Translation[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, v.56; No.297(01):34-41. 

  

Research Interests: 

Natural language processing. 

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