Research of types and current state of machine translation

Research Article
Open access

Research of types and current state of machine translation

Yanran Wang 1*
  • 1 Beijing University of Chemical Technology    
  • *corresponding author 2021040107@buct.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/37/20230479
ACE Vol.37
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-299-2
ISBN (Online): 978-1-83558-300-5

Abstract

The background of machine translation dates back to the 1950s when scientists began exploring the use of computers for translation. Motivated by various challenges and needs, such as the high cost of manual translation and cross-cultural communication, machine translation has become a pivotal field. This overview delves into the research background, content, methods, key figures, conclusions, and future prospects of machine translation. It summarizes automatic evaluation metrics, corpus construction, and transfer learning, all of which contribute to enhancing translation performance. Currently, there are three mainstream categories of methods, which include rule-based translation, statistical translation, and neural network-based translation. The rule-based translation method relies on language rules and dictionaries for translation. Statistical machine translation involves the use of extensive bilingual corpora for identification and translation. The conclusion emphasizes the potential of neural machine translation, yet acknowledges challenges in diverse languages, low-resource languages, and specialized terminology.

Keywords:

Machine translation, types, analysis, deep learning

Wang,Y. (2024). Research of types and current state of machine translation. Applied and Computational Engineering,37,95-101.
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References

[1]. Liu Qun. Review and Prospect of Machine Translation Research. Journal of Chinese Information Processing, 2018, 29(1): 1-9.

[2]. Hutchins W. J. Machine Translation: A Concise History. In Machine Translation History. 3-48, 2007.

[3]. Bai Shize. Development Process of Machine Translation Technology. Computer Engineering, 2017, 31(3): 1-3.

[4]. Brown, T. B., Mann, B., Ryder, N., Subbiah, Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, 33.

[5]. Hutchins W. J. Machine Translation: A Concise History. John Benjamins Publishing, 1986.

[6]. Koehn, P., & Knight, K. Empirical methods for compound splitting. Conference of the European Chapter of the Association for Computational Linguistics 2003. 187-193.

[7]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Attention is all you need. Advances in neural information processing systems, 2017, 30.

[8]. Radford, A., & Salimans, T. Improving language understanding by generative pretraining. OpenAI, 2018.

[9]. Rajman, M. Experience with a rule-based machine translation system for French-English. Machine translation, 1998 3(2-3), 163-184.

[10]. Liu, Y., & Jiang, W. Rule-based statistical machine translation. Tsinghua University. 2013

[11]. Hutchins, W. J. Machine translation: A concise history. In History of machine translation, 2007 3-48.

[12]. Koehn, P., & Hoang, H. Factored translation models. 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 868-876.

[13]. Och, F. J., & Ney, H. A systematic comparison of various statistical alignment models. Computational Linguistics, 2003 29(1), 19-51.

[14]. Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer, R. L. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 1993,19(2), 263-311.

[15]. Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z.. Google's multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 2017, 5, 339-351.

[16]. Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. Convolutional Sequence to Sequence Learning. 34th International Conference on Machine Learning, 2017, 1243-1252.

[17]. Vaswani, A., Bengio, S., Boulanger-Lewandowski, N., & Bengio, Y. Axiomatic Memory Networks. arXiv preprint arXiv:1310.6299, 2013.

Cite this article

Wang,Y. (2024). Research of types and current state of machine translation. Applied and Computational Engineering,37,95-101.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-299-2(Print) / 978-1-83558-300-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.37
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Liu Qun. Review and Prospect of Machine Translation Research. Journal of Chinese Information Processing, 2018, 29(1): 1-9.

[2]. Hutchins W. J. Machine Translation: A Concise History. In Machine Translation History. 3-48, 2007.

[3]. Bai Shize. Development Process of Machine Translation Technology. Computer Engineering, 2017, 31(3): 1-3.

[4]. Brown, T. B., Mann, B., Ryder, N., Subbiah, Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, 33.

[5]. Hutchins W. J. Machine Translation: A Concise History. John Benjamins Publishing, 1986.

[6]. Koehn, P., & Knight, K. Empirical methods for compound splitting. Conference of the European Chapter of the Association for Computational Linguistics 2003. 187-193.

[7]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Attention is all you need. Advances in neural information processing systems, 2017, 30.

[8]. Radford, A., & Salimans, T. Improving language understanding by generative pretraining. OpenAI, 2018.

[9]. Rajman, M. Experience with a rule-based machine translation system for French-English. Machine translation, 1998 3(2-3), 163-184.

[10]. Liu, Y., & Jiang, W. Rule-based statistical machine translation. Tsinghua University. 2013

[11]. Hutchins, W. J. Machine translation: A concise history. In History of machine translation, 2007 3-48.

[12]. Koehn, P., & Hoang, H. Factored translation models. 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 868-876.

[13]. Och, F. J., & Ney, H. A systematic comparison of various statistical alignment models. Computational Linguistics, 2003 29(1), 19-51.

[14]. Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer, R. L. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 1993,19(2), 263-311.

[15]. Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z.. Google's multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 2017, 5, 339-351.

[16]. Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. Convolutional Sequence to Sequence Learning. 34th International Conference on Machine Learning, 2017, 1243-1252.

[17]. Vaswani, A., Bengio, S., Boulanger-Lewandowski, N., & Bengio, Y. Axiomatic Memory Networks. arXiv preprint arXiv:1310.6299, 2013.

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