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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 8
2288-2292,2320

Research on Chinese-Korean machine translation based on reinforcement learning and machine translation quality estimation

Li Feiyu
Zhao Yahui
Cui Rongyi
Yang Feiyang
Intelligent Information Processing Laboratory, Yanbian University, Yanji Jilin 133002, China
DOI: 10.19734/j.issn.1001-3695.2020.12.0411

Abstract

This paper proposed a Chinese-Korean neural machine translation model called QE-Transformer based on reinforcement learning and machine translation quality estimation to solve the problems about exposure bias and diversity in current machine translation. First of all, this model introduced an evaluation mechanism into the sentence level to guide the model when the prediction couldn't converge completely on the ground truth. And furthermore, it used reinforcement learning as a guiding strategy to optimize the sequence of objectives at the sentence level. Finally, by using monolingual data and different granularity data preprocessing in the training process, it alleviated the problem of data sparsity. Experimental results show that the QE-Transformer can improve effectively the Chinese-Korean machine translation performance compared with Transformer, the BLEU value increases by 5.39 and the QE score decreases by 5.16 in the direction of the Chinese to Korean machine translation, the BLEU value increases by 2.73 and the QE score decreases by 2.82 in the direction of the Korean to Chinese machine translation.

Foundation Support

国家语委“十三五”科研资助项目(YB135-76)
延边大学外国语言文学一流学科建设项目(18YLPY13)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0411
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 8
Section: Algorithm Research & Explore
Pages: 2288-2292,2320
Serial Number: 1001-3695(2021)08-007-2288-05

Publish History

[2021-08-05] Printed Article

Cite This Article

李飞雨, 赵亚慧, 崔荣一, 等. 基于强化学习和机器翻译质量评估的中朝机器翻译研究 [J]. 计算机应用研究, 2021, 38 (8): 2288-2292,2320. (Li Feiyu, Zhao Yahui, Cui Rongyi, et al. Research on Chinese-Korean machine translation based on reinforcement learning and machine translation quality estimation [J]. Application Research of Computers, 2021, 38 (8): 2288-2292,2320. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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