Advertisement

Bilingual Machine Translation: English to Bengali

  • Sauvik BalEmail author
  • Supriyo MahantaEmail author
  • Lopa Mandal
  • Ranjan Parekh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)

Abstract

The present work proposes a methodology of machine translation system which takes English sentences as input and produces appropriate Bengali sentences as output using natural language processing (NLP) techniques. It first uses a parse tree for syntactic analysis of the sentence structure and then applies semantic analysis for extracting the meaning of the words. An inverse function is then provided to fit that into the Bengali syntax. A dictionary as a separate file is used for mapping between the English words and their Bengali counterparts. The novelty of the present work lies in the fact that it combines both a syntax-based and a meaning-based analysis to arrive at the proper translation. The effectiveness of the algorithm has been demonstrated with examples of different English sentence conversions with several rules, and the results have been compared with that of the Google translator to show the improvements achieved.

Keywords

POS tagging Machine translation Parse tree Rule-based system 

References

  1. 1.
    Adak, C.: An advanced approach for rule based English to Bengali machine translation. J. Nat. Conf. Adv. Comput. Eng. Res. 01–09 (2013)Google Scholar
  2. 2.
    Fung, P., Schultz, T.: Multilingual spoken language processing [Challenges for multilingual systems]. IEEE Signal Process. Mag. (2008)Google Scholar
  3. 3.
    Lee, H.-Y., Lee, L.-S.: Improved semantic retrieval of spoken content by document/query expansion with random walk over acoustic similarity graphs. IEEE/ACM Trans. Audio Speech Lang. Process. 22(1) (2014)CrossRefGoogle Scholar
  4. 4.
    Muntarina, K.Md., Moazzam, G.Md., Bhuiyan, A.-A.: Tense based english to Bangla translation using MT system. Int. J. Eng. Sci. Invent. ISSN (Online): 2319–6734, ISSN (Print): 2319–6726Google Scholar
  5. 5.
    Zhang, S., Mahmut, G., Wang, D., Hamdulla, A.: Memory-augmented Chinese-Uyghur neural machine translation, APSIPA ASC (2017)Google Scholar
  6. 6.
    Deena, S., Ng, R.W.M., Madhyastha, P., Specia, L., Hain, T.: Exploring the use of acoustic embeddings in neural machine translation, (ASRU). IEEE (2017)Google Scholar
  7. 7.
    Macabante, D.G., Tambanillo, J.C., Cruz, A.D., Ellema, N., Octaviano Jr. M., Rodriguez, R., Edita Roxas, R.: Bi-directional English-Hiligaynon statistical machine translation, (TENCON). Malaysia, Nov 5–8 2017Google Scholar
  8. 8.
    Anwarus Salam, K.Md., Khan, M., Nishino, T.: Example based English-Bengali machine translation using WordNet. https://pdfs.semanticscholar.org/3caf/770def3e398b7ca5396e9f79aa6bbab1cc6b.pdf?_ga=2.147250417.779477973.1533393833-37296364.1533231961
  9. 9.
    Zhou, G., Xie, Z., He, T., Zhao, J., Hu, X.T.: Learning the multilingual translation representations for question retrieval in community question answering via non-negative matrix factorization. IEEE Trans. Audio Speech Lang. Process. 24(7) (2016)CrossRefGoogle Scholar
  10. 10.
    Ghadage, Y.H., Shelke, S.D.: Speech to text conversion for multilingual languages. In: International Conference on Communication and Signal Processing. India, 6–8 April 2016Google Scholar
  11. 11.
    Lee, L., Glass, J., Lee, H., Chan, C.: Spoken content retrieval—beyond cascading speech recognition with text retrieval. IEEE/ACM Trans. Audio Speech Lang. Process. 23(9) (2015)Google Scholar
  12. 12.
    Young, T., Hazarika, D., Poria, S., Cambri, E.: Recent Trends in Deep Learning Based Natural Language Processing. [cs.CL], 16 Aug 2017Google Scholar
  13. 13.
    Hung, B.T., Minh, N.L., Shimazu, A.: Sentence splitting for vietnamese-english machine translation. In: Fourth International Conference on Knowledge and Systems Engineering (2012)Google Scholar
  14. 14.
    Zhu, Z., Bernhard, D., Gurevych, I.: A monolingual tree-based translation model for sentence simplification. In: Proceedings of the 23rd International Conference on Computational Linguistics. Coling (2010)Google Scholar
  15. 15.
    Xiong, H., Xu, W., Mi, H., Liu, Y., Lu, Q.: Sub-sentence division for tree-based machine translation. In: Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP, Short Papers, pp. 137–140. Singapore (2009)Google Scholar
  16. 16.
    Thenmozhi, D., Aravindan, C.: Paraphrase identification by using clause-based similarity features and machine translation metrics. Comput. J. Adv. Access published 5 Oct 2015Google Scholar
  17. 17.
    Kavirajan, B., Anand Kumar, M., Soman, K.P., Rajendran, S., Vaithehi, S.: Improving the rule based machine translation system using sentence simplification (English to Tamil). Int. J. Comput. Appl. (0975–8887) 25(8) (2011)Google Scholar
  18. 18.
    Tur, G., Hakkani-Tur, D., Heck, L., Parthasarathy, S.: Sentence Simplification for Spoken Language Understanding, Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference, 12 July 2011Google Scholar
  19. 19.
    Mitra, M., Mitra, D.: Oxford English-English-Bengali Dictionary, Oxford University Press. ISBN: 9780195689648Google Scholar
  20. 20.
    Basu, S.: English Tutor, Class VI, chhayaprakashanipvt ltdGoogle Scholar
  21. 21.
  22. 22.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of Engineering & ManagementJaipurIndia
  2. 2.Institute of Engineering & ManagementKolkataIndia
  3. 3.Jadavpur UniversityKolkataIndia

Personalised recommendations