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Analysis and evaluation on the quality of news text machine translation based on neural network

  • Liu TingtingEmail author
  • Xiao Mengyu
Article

Abstract

Machine translation quality estimation is an important task in natural language processing. Unlike traditional automatic machine translation evaluation methods, the quality of machine translation is evaluated by the translation quality estimation method without using manual reference translation. In view of the fact that the feature extraction of current sentence-level machine translation quality estimation relies heavily on linguistic analysis, which leads to insufficient generalization ability and restricts the performance of subsequent support vector regression algorithm, it is proposed to extract the features of sentence vectors by using contextual word prediction model and matrix decomposition model in deep learning, and combine them with the features of recurrent neural network language model to improve the correlation between automatic estimation of translation quality and manual evaluation. The experimental results on WMT15 and WMT16 translation quality estimation subtask data sets show that the performance statistics of the method of extracting sentence vector features by adopting context word prediction model are consistently superior to that of the traditional Quest method and continuous space language model sentence vector feature extraction method. It reveals that the proposed feature extraction method not only requires no linguistic analysis, but also significantly improves the effect of translation quality estimation.

Keywords

Neural network Text machine Translation quality Analysis and evaluation 

Notes

Acknowledgements

Teaching Research Project of Fuyang Normal University No.2012JYXM44; Scientific Research Project of Fuyang Normal University 2012WBZX05.

References

  1. 1.
    Abdulhay E, Alafeef M, Alzghoul L, Momani MA, Al Abdi R, Arun Kumar N, Munoz R, de Albuquerque VHC (2018) Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition. Neural Comput & Applic.  https://doi.org/10.1007/s00521-018-3738-0
  2. 2.
    Arunkumar N, Mohammed MA, Abd Ghani MK et al (2018) K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput.  https://doi.org/10.1007/s00500-018-3618-7
  3. 3.
    Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency Computat Pract Exper:e4962.  https://doi.org/10.1002/cpe.4962
  4. 4.
    Bagyalakshmi G, Rajkumar G, Arunkumar N, Easwaran M, Narasimhan K, Elamaran V, Solarte M, Hernández I, Ramirez-Gonzalez G (2018) Network vulnerability analysis on brain signal/image databases using Nmap and Wireshark tools. IEEE Access 6:57144–57151CrossRefGoogle Scholar
  5. 5.
    Baker K, Bloodgood M, Dorr BJ et al (2012) Use of modality and negation in semantically-informed syntactic MT[J]. Computational Linguistics 38(2):870–879CrossRefGoogle Scholar
  6. 6.
    Banea C, Mihalcea R, Wiebe J, et al (2009) Multilingual Subjectivity Analysis Using Machine Translation.[C]// Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Proceedings of the Conference, 25–27 October 2008, Honolulu, Hawaii, Usa, A Meeting of Sigdat, A Special Interest Group of the ACL. DBLP, 127–135Google Scholar
  7. 7.
    Cao DL, Tang-Qiu LI, Shi XD et al (2004) Practice and analysis in evaluation of machine translation[J]. Application Research of Computers 21(3):29–32Google Scholar
  8. 8.
    Dongdong J, Arunkumar N, Wenyu Z, Beibei L, Xinlei Z, Guangjian Z (2019) Semantic clustering fuzzy c means spectral model based comparative analysis of cardiac color ultrasound and electrocardiogram in patients with left ventricular heart failure and cardiomyopathy. Futur Gener Comput Syst 92:324–328CrossRefGoogle Scholar
  9. 9.
    Elamaran V, Arunkumar N, Hussein AF, Solarte M, Ramirez-Gonzalez G (2018) Spectral fault recovery analysis revisited with Normal and abnormal heart sound signals. IEEE Access 6:62874–62879CrossRefGoogle Scholar
  10. 10.
    Elamaran V, Arunkumar N, Babu GV, Balaji VS, Gómez J, Figueroa C, Ramirez-Gonzalez G (2018) Exploring DNS, HTTP, and ICMP response time computations on brain signal/image databases using a packet sniffer tool. IEEE Access 6:59672–59678CrossRefGoogle Scholar
  11. 11.
    El-Haj M, Kruschwitz U, Fox C (2015) Creating language resources for under-resourced languages: methodologies, and experiments with Arabic[J]. Language Resources & Evaluation 49(3):549–580CrossRefGoogle Scholar
  12. 12.
    Elhoseny M, Shankar K, Lakshmanaprabu SK, Andino Maseleno NA (2018) Hybrid optimization with cryptography encryption for medical image security in internet of things. Neural Comput & Applic:1–15.  https://doi.org/10.1007/s00521-018-3801-x
  13. 13.
    Haoyu L, Jianxing L, Arunkumar N, Hussein AF, Jaber MM (2018) An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability. Futur Gener Comput Syst.  https://doi.org/10.1016/j.future.2018.12.001 CrossRefGoogle Scholar
  14. 14.
    Jiajie L, Narasimhan K, Elamaran V, Arunkumar N, Solarte M, Ramirez-Gonzalez G (2018) Clinical decision support system for alcoholism detection using the analysis of EEG signals. IEEE Access 6:61457–61461CrossRefGoogle Scholar
  15. 15.
    Kell DB (2010) Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells[J]. FEBS J 273(5):873–894CrossRefGoogle Scholar
  16. 16.
    Khamparia A, Singh A, Anand D et al (2018) A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Comput & Applic.  https://doi.org/10.1007/s00521-018-3896-0
  17. 17.
    Lakshmanaprabu S.K, Sachi Mohanty, Shankar K, Arunkumar N, Gustavo Ramirez, "Optimal deep learning model for classification of lung Cancer on CT images, Futur Gener Comput Syst, Vol. 92, 2019, Pg. 374–382Google Scholar
  18. 18.
    Lin CY, Och FJ 2004 Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics[J]. Proceedings of Annual Meeting of the Association for Computational Linguistics, 605–612.Google Scholar
  19. 19.
    Mohammed MA, Abd Ghani MK, Arunkumar N, Hamed RI, Mostafa SA, Abdullah MK, Burhanuddin MA (2018) Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput.  https://doi.org/10.1007/s11227-018-2587-z
  20. 20.
    Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput & Applic:1–7.  https://doi.org/10.1007/s00521-018-3689-5
  21. 21.
    Peixoto SA, Filho PPR, Arun Kumar N, de Albuquerque VHC (2018) Automatic classification of pulmonary diseases using a structural co-occurrence matrix. Neural Comput & Applic:1–11.  https://doi.org/10.1007/s00521-018-3736-2
  22. 22.
    Pereira RF, da Silva Filho VER, Moura LB, Kumar NA, de Alexandria AR, de Albuquerque VHC (2018) Automatic quantification of spheroidal graphite nodules using computer vision techniques. J Supercomput.  https://doi.org/10.1007/s11227-018-2579-z
  23. 23.
    Popović M, Avramidis E, Burchardt A et al (2014) Involving language professionals in the evaluation of machine translation[J]. Language Resources & Evaluation 48(4):541–559CrossRefGoogle Scholar
  24. 24.
    U. Rajendra Achary, Yuki Hagiwara, Sunny Nitin Deshpande, S. Suren, Joel En Wei Koh, Shu Lih Oh, N. Arunkumar, Edward J. Ciaccio, Choo Min Lim., "Characterization of focal EEG signals: a review"., Futur Gener Comput Syst, Vol. 91, Feb 2019, pg. 290–299Google Scholar
  25. 25.
    Santamaria-Granados L, Munoz-Organero M, Ramirez-Gonzalez G, Abdulhay E, Arunkumar N (2018) Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS). IEEE Access.  https://doi.org/10.1109/ACCESS.2018.2883213 CrossRefGoogle Scholar
  26. 26.
    Sathishkumar BR, Sundaravadivazhagan B, Martin B, Sasi G, Chandrasekar M, Kumar SR, … Arunkumar N Revisiting computer networking protocols by wireless sniffing on brain signal/image portals. Neural Comput & Applic:1–13.  https://doi.org/10.1007/s00521-018-3919-x
  27. 27.
    Venkatraman V, Arunkumar N, Chantre-Astaiza A, Muñoz-Mazón AI, Fuentes-Moraleda L, Khan MS (2018) Mapping the structure and evolution of heavy vehicle research: a scientometric analysis and visualisation, Int. J. Heavy Vehicle Systems, Vol. 25, Nos. 3/4, pp.344–368Google Scholar
  28. 28.
    Wu Z, Wang H, Arunkumar N (2019) Bayesian analysis model for the use of anesthetic analgesic drugs in cancer patients based on geometry reconstruction. Futur Gener Comput Syst 93:170–175CrossRefGoogle Scholar
  29. 29.
    Zhang WN, Ming ZY, Zhang Y et al (2016) Capturing the semantics of key phrases using multiple languages for question retrieval[J]. IEEE Transactions on Knowledge & Data Engineering 28(4):888–900CrossRefGoogle Scholar
  30. 30.
    Zhou D, Ravey A, Al-Durra A, Gao F (2017) A comparative study of extremum seeking methods applied to energy management strategy of fuel cell hybrid electric vehicles. Energy Convers Manag 151:778–790CrossRefGoogle Scholar
  31. 31.
    Zhou D, Al-Durra A, Zhang K, Ravey A, Gao F (2018) Online remaining useful life prediction of proton exchange membrane fuel cells using a novel robust methodology. J Power Sources 399(30):314–328CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Foreign LanguagesFuyang Normal UniversityFuyangChina
  2. 2.College of Information and EngineeringFuyang Normal UniversityFuyangChina

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