skip to main content
research-article

Deep Learning in Computational Linguistics for Chinese Language Translation

Published:10 March 2023Publication History
Skip Abstract Section

Abstract

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

REFERENCES

  1. [1] Maulud D. H., Zeebaree S. R., Jacksi K., Sadeeq M. A. M., and Sharif K. H.. 2021. State of art for semantic analysis of natural language processing. Qubahan Academic Journal 1, 2 (2021), 2128.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Dalpiaz F., Ferrari A., Franch X., and Palomares C.. 2018. Natural language processing for requirements engineering: The best is yet to come. IEEE Software 35, 5 (2018), 115119.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Young T., Hazarika D., Poria S., and Cambria E.. 2018. Recent trends in deep learning-based natural language processing. IEEE Computational Intelligence Magazine 13, 3 (2018), 5575.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Aqlan F., Fan X., Alqwbani A., and Al-Mansoub A.. 2019. Arabic–Chinese neural machine translation: Romanized Arabic as subword unit for Arabic-sourced translation. IEEE Access 7, (2019), 133122133135.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Marquez J. L. J., Carrasco I. G., and Cuadrado J. L. L.. 2018. Challenges and opportunities in analytic-predictive environments of big data and natural language processing for social network rating systems. IEEE Latin America Transactions 16, 2 (2018), 592597.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Zeng Z., Deng Y., Li X., Naumann T., and Luo Y.. 2018. Natural language processing for EHR-based computational phenotyping. IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, 1 (2018), 139153.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Zhang W. E., Sheng Q. Z., Alhazmi A., and Li C.. 2020. Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology 11, 3 (2020), 141.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Gaur M., Faldu K., and Sheth A.. 2021. Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable? IEEE Internet Computing 25, 1 (2021), 5159.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Huang W. C., Hayashi T., Wu Y. C., Kameoka H., and Toda T.. 2021. Pretraining techniques for sequence-to-sequence voice conversion. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021), 745755.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Li S., Zhao J., Shi G., Tan Y., Xu H., Chen G., and Lin Z.. 2019. Chinese grammatical error correction based on convolutional sequence to sequence model. IEEE Access 7 (2019), 7290572913.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Liu J., Sun T., Fan X., Zhang Y., and Lai B.. 2021. A modified simulation model for predicting the FDS of transformer oil-paper insulation under nonuniform aging. IEEE Transactions on Instrumentation and Measurement 70 (2021), 19.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Yan H., Wan J., Zhang C., Tang S., Hua Q., and Wang Z.. 2018. Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access 6 (2018), 1719017197.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Wu Y., Luo Y., Chaudhari G., Rivenson Y., Calis A., De Haan K., and Ozcan A.. 2019. Bright-field holography: Cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light: Science & Applications 8, 1 (2019), 17.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Shao Y. and Chou K. C.. 2020. pLoc_deep-mvirus: A CNN model for predicting subcellular localization of virus proteins by deep learning. Natural Science 12, 6 (2020), 388399.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Sremac S., Tanackov I., Kopić M., and Radović D.. 2018. ANFIS model for determining the economic order quantity. Decision Making: Applications in Management and Engineering 1, 2 (2018), 8192.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Ghosh K., Stuke A., Todorović M., Jørgensen P. B., Schmidt M. N., Vehtari A., and Rinke P.. 2019. Deep learning spectroscopy: Neural networks for molecular excitation spectra. Advanced Science 6, 9 (2019), 1801367.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Otter D. W., Medina J. R., and Kalita J. K.. 2020. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems 32, 2 (2020), 604624.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Ramati I. and Pinchevski A.. 2018. Uniform multilingualism: A media genealogy of Google Translate. New Media & Society 20, 7 (2018), 25502565.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Lin J. C. W., Shao Y., Zhou Y., Pirouz M., and Chen H. C.. 2019. A Bi-LSTM mention hypergraph model with encoding schema for mention extraction. Engineering Applications of Artificial Intelligence 85 (2019), 175181.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Khan S. N. and Usman I.. 2019. A model for English to Urdu and Hindi machine translation system using translation rules and artificial neural network. International Arab Journal of Information Technology 16, 1 (2019), 125131.Google ScholarGoogle Scholar
  21. [21] Si C., Zhang Z., Chen Y., Qi F., Wang X., Liu Z., and Sun M.. 2021. SHUOWEN-JIEZI: Linguistically informed tokenizers for Chinese language model pretraining. arXiv preprint arXiv:2106.00400 (2021).Google ScholarGoogle Scholar
  22. [22] Shao Y., Lin J. C. W., Srivastava G., Jolfaei A., Guo D., and Hu Y.. 2021. Self-attention-based conditional random fields latent variables model for sequence labeling. Pattern Recognition Letters 145 (2021), 157164.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Lin J. C. W., Shao Y., Djenouri Y., and Yun U.. 2021. ASRNN: A recurrent neural network with an attention model for sequence labeling. Knowledge-Based Systems 212 (2021), 106548.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Santy S. and Bhattacharya P.. 2021. A discussion on building practical NLP leaderboards: The case of machine translation. arXiv preprint arXiv:2106.06292 (2021).Google ScholarGoogle Scholar
  25. [25] Lin J. C. W., Shao Y., Zhang J., and Yun U.. 2020. Enhanced sequence labeling based on latent variable conditional random fields. Neurocomputing 403 (2020), 431440.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Zhao S., Hu M., Cai Z., Zhang Z., Zhou T., and Liu F.. 2021. Enhancing Chinese character representation with lattice-aligned attention. IEEE Transactions on Neural Networks and Learning Systems. Early access, October 5, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Wu S., Roberts K., Datta S., Du J., Ji Z., Si Y., and Xu H.. 2020. Deep learning in clinical natural language processing: A methodical review. Journal of the American Medical Informatics Association 27, 3 (2020), 457470.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Azalia F. Y., Bijaksana M. A., and Huda A. F.. 2019. Name indexing in Indonesian translation of Hadith using named entity recognition with naïve Bayes classifier. Procedia Computer Science 157 (2019), 142149.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Kehl K. L., Elmarakeby H., Nishino M., Van Allen E. M., Lepisto E. M., Hassett M. J., and Schrag D.. 2019. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncology 5, 10 (2019), 14211429.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Sorin V., Barash Y., Konen E., and Klang E.. 2020. Deep learning for natural language processing in radiology—Fundamentals and a systematic review. Journal of the American College of Radiology 17, 5 (2020), 639648.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Tong X. and Zhou K.. 2019. Deep learning in computer graphics. IEEE Computer Graphics and Applications 39, 2 (2019), 2525.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Bashar A.. 2019. Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence 1, 2 (2019), 7382.Google ScholarGoogle Scholar
  33. [33] Ma J., Song Y., Tian X., Hua Y., Zhang R., and Wu J.. 2020. Survey on deep learning for pulmonary medical imaging. Frontiers of Medicine 14 (2020), 450469.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Jaf S. and Calder C.. 2019. Deep learning for natural language parsing. IEEE Access 7 (2019), 131363131373.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Zhang H., Guan R., Zhou F., Liang Y., Zhan Z. H., Huang L., and Feng X.. 2019. Deep residual convolutional neural network for protein-protein interaction extraction. IEEE Access 7 (2019), 8935489365.Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Guo X., Zhang H., Yang H., Xu L., and Ye Z.. 2019. A single attention-based combination of CNN and RNN for relation classification. IEEE Access 7 (2019), 1246712475.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Liu H., Xu Y., Zhang Z., Wang N., Huang Y., Hu Y., and Chen H.. 2020. A natural language processing pipeline of Chinese free-text radiology reports for liver cancer diagnosis. IEEE Access 8 (2020), 159110159119.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Hossain M. Z., Sohel F., Shiratuddin M. F., Laga H., and Bennamoun M.. 2021. Text to image synthesis for improved image captioning. IEEE Access 9 (2021), 6491864928.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Yahya Z., Hassan M., Younis S., and Shafique M.. 2020. Probabilistic analysis of targeted attacks using transform-domain adversarial examples. IEEE Access 8 (2020), 3385533869.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Luo Y. and Xiang Y.. 2020. Application of data mining methods in Internet of Things technology for the translation systems in traditional ethnic books. IEEE Access 8 (2020), 9339893407.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Deep Learning in Computational Linguistics for Chinese Language Translation

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 3
          March 2023
          570 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3579816
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 10 March 2023
          • Online AM: 15 March 2022
          • Accepted: 16 February 2022
          • Received: 13 June 2021
          Published in tallip Volume 22, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
        • Article Metrics

          • Downloads (Last 12 months)126
          • Downloads (Last 6 weeks)10

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!