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Research on statistical machine translation model based on deep neural network

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Abstract

With the increase of translation demand, the advancement of information technology, the development of linguistic theories and the progress of natural language understanding models in artificial intelligence research, machine translation has gradually gained worldwide attention. However, at present, machine translation research still has problems such as insufficient bilingual data and lack of effective feature representation, which affects the further improvement of key modules of machine translation such as word alignment, sequence adjustment and translation modelling. The effect of machine translation is still unsatisfactory. As a new machine learning method, deep neural network can automatically learn abstract feature representation and establish a complex mapping relationship between input and output signals, which provides a new idea for statistical machine translation research. Firstly, the multi-layer neural network and the undirected probability graph model are combined, and the similarity and context information of vocabulary are effectively utilized to model the word alignment more fully, and the word alignment model named NNWAM is constructed. Secondly, the low dimension will be used. The feature representation is combined with other features into a linearly ordered pre-ordering model to construct the pre-ordering model named NNPR. Finally, the word alignment model and the pre-ordering model are combined in the same deep neural network framework to form DNNAPM, a statistical machine translation model based on deep neural networks. The experimental results show that the statistical machine translation model based on deep neural network has better effect, faster convergence and better reliability than the comparison model algorithm.

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References

  1. Claycomb J, Abreu-Goodger C, Buck AH (2017) RNA-mediated communication between helminths and their hosts: the missing links. RNA Biol 14(4):436–441

    Article  Google Scholar 

  2. Su J, Zeng J, Xiong D et al (2018) A hierarchy-to-sequence attentional neural machine translation model. IEEE/ACM Trans Audio Speech Lang Process 26(3):623–632

    Article  Google Scholar 

  3. Lo B, Zettler P, Cedars MI et al (2010) A new era in the ethics of human embryonic stem cell research. Stem Cells 23(10):1454–1459

    Article  Google Scholar 

  4. Curtmola R, Garay J, Kamara S et al (2011) Searchable symmetric encryption: improved definitions and efficient constructions. J Comput Secur 19(5):895–934

    Article  Google Scholar 

  5. Sun Y, Xu J, Qiang H et al (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141:217–226

    Article  Google Scholar 

  6. Chakraborty S, Khasidashvili Z, Seger CJH et al (2017) Symbolic trajectory evaluation for word-level verification: theory and implementation. Form Methods Syst Des 50(2–3):1–36

    MATH  Google Scholar 

  7. Kim K, Park EJ, Shin JH et al (2017) Divergence-based fine pruning of phrase-based statistical translation model. Comput Speech Lang 41(C):146–160

    Article  Google Scholar 

  8. Dai X-G, Wang P (2017) A new classification of large-scale climate regimes around the Tibetan Plateau based on seasonal circulation patterns. Adv Clim Change Res 8(1):26–36

    Article  Google Scholar 

  9. Ashraf N, Ahmad M (2015) Machine translation techniques and their comparative study. Int J Comput Appl 125(7):25–31

    Google Scholar 

  10. Gao S, Yang X, Yu Z et al (2017) Chinese-Naxi machine translation method based on Naxi dependency language model. Int J Mach Learn Cybern 8(1):333–342

    Article  Google Scholar 

  11. Dong W, Chi M (2017) Long short-term memory with quadratic connections in recursive neural networks for representing compositional semantics. IEEE Access 5:16077–16083

    Article  Google Scholar 

  12. Zhang X, Liang Y, Chen L et al (2017) Recursive autoencoders-based unsupervised feature learning for hyperspectral image classification. IEEE Geosci Remote Sens Lett 14(11):1928–1932

    Article  Google Scholar 

  13. Wang S, Cong Y, Cao J et al (2016) Scalable gastroscopic video summarization via similar-inhibition dictionary selection. Artif Intell Med 66:1–13

    Article  Google Scholar 

  14. Guzmán F, Joty S, Màrquez L et al (2017) Machine translation evaluation with neural networks. Comput Speech Lang 45:180–200

    Article  Google Scholar 

  15. Ding C, Sakanushi K, Touji H et al (2016) Inter-, intra-, and extra-chunk pre-ordering for statistical Japanese-to-English machine translation. ACM Trans Asian Low Resour Lang Inf Process 15(3):1–28

    Article  Google Scholar 

  16. Chong CC, Lim TY, Soon LK et al (2017) Meaning preservation in example-based machine translation with structural semantics. Expert Syst Appl 78:242–258

    Article  Google Scholar 

  17. Hasler E, Gispert AD, Stahlberg F et al (2017) Source sentence simplification for statistical machine translation. Comput Speech Lang 45(C):221–235

    Article  Google Scholar 

  18. Song Z (2017) The research on key technologies of chinese heavy-lift launch vehicle control system. Aerosp China 18(2):13–22

    Google Scholar 

  19. Marmanis D, Datcu M, Esch T et al (2016) Deep learning earth observation classification using imagenet pretrained networks. IEEE Geosci Remote Sens Lett 13(1):105–109

    Article  Google Scholar 

  20. Weber M, Fackeldey K, Schütte C (2017) Set-free Markov state model building. J Chem Phys 146(12):124133

    Article  Google Scholar 

  21. Shen M, Dan Y (2017) A finite frequency approach to control of Markov jump linear systems with incomplete transition probabilities. Appl Math Comput 295:53–64

    MathSciNet  MATH  Google Scholar 

  22. Kang L, Xu L, Zhao J (2018) Co-extracting opinion targets and opinion words from online reviews based on the word alignment model. IEEE Trans Knowl Data Eng 27(3):636–650

    Google Scholar 

  23. Wu ZG, Ju HP, Su H et al (2012) Passivity analysis of Markov jump neural networks with mixed time-delays and piecewise-constant transition rates. Nonlinear Anal Real World Appl 13(5):2423–2431

    Article  MathSciNet  MATH  Google Scholar 

  24. Mo YY, Guo JY, Yu ZT et al (2015) A bilingual word alignment algorithm of Vietnamese–Chinese based on feature constraint. Int J Mach Learn Cybern 6(4):537–543

    Article  Google Scholar 

  25. Liu Y (2019) Digital image recognition based on improved cognitive neural network. Transl Neurosci 10(1):125–128

    Article  Google Scholar 

  26. He W (2019) Computational neuroscience applied in surface roughness fiber optic sensor. Transl Neurosci 10(1):70–75

    Article  Google Scholar 

  27. Chen MC, Lu SQ, Liu QL (2018) Global regularity for a 2D model of electro-kinetic fluid in a bounded domain. Acta Mathematicae Applicatae Sinica, Engl Ser 34(2):398–403

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Foreign Languages and Cultures, Chongqing Three Gorges University, Wanzhou, Chongqing, 404100, China

    Ying Xia

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  1. Ying Xia
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Correspondence to Ying Xia.

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Xia, Y. Research on statistical machine translation model based on deep neural network. Computing 102, 643–661 (2020). https://doi.org/10.1007/s00607-019-00752-1

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