Skip to main content

Direct-Bridge Combination Scenario for Persian-Spanish Low-Resource Statistical Machine Translation

  • Conference paper
  • First Online:
Artificial Intelligence and Natural Language (AINL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 930))

Included in the following conference series:

Abstract

This paper investigates the idea of making effective use of bridge language technique to respond to minimal parallel-resource data set bottleneck reality to improve translation quality in the case of Persian-Spanish low-resource language pair using a well-resource language such as English as the bridge one. We apply the optimized direct-bridge combination scenario to enhance the translation performance. We analyze the effects of this scenario on our case study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmadnia, B., Serrano, J., Haffari, G.: Persian-Spanish low-resource statistical machine translation through English as pivot language. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP), pp. 24–30 (2017)

    Google Scholar 

  2. Babych, B., Hartley, A., Sharoff, S., Mudraya, O.: Assisting translators in indirect lexical transfer. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL) (2007)

    Google Scholar 

  3. Koehn, P.: Europarl: a parallel corpus for statistical machine translation. In Proceedings of the 10th Machine Translation Summit (AAMT), Phuket, Thailand, pp. 79–86 (2005)

    Google Scholar 

  4. Matusov, E., et al.: System combination for machine translation of spoken and written language, pp. 1222–1237. In: Proceedings of Transactions on Audio, Speech and Language (IEEE) (2008)

    Google Scholar 

  5. Kumar, S., Och, F., Macherey, W.: Improving word alignment with bridge languages. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing (EMNLP), and Computational Natural Language Learning, pp. 42–50 (2007)

    Google Scholar 

  6. Paul, M., Yamamoto, H., Sumita, E., Nakamura, S.: On the importance of pivot language selection for statistical machine translation. In: Proceedings of Human Language Technologies: The Annual Conference of the North American Chapters of the Association for Computational Linguistics (HLT-NAACL), pp. 221–224 (2009)

    Google Scholar 

  7. Zhu, X., He, Z., Wu, H., Zhu, C.: Improving pivot-based statistical machine translation by pivoting the co-occurrence count of phrase pairs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1665–1645 (2014)

    Google Scholar 

  8. Saralegi, X., Manterola, I., Vicente, I.: Analyzing methods for improving precision of pivot based bilingual dictionaries. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 846–856 (2011)

    Google Scholar 

  9. Tofighi, S., Bakhshaei, S., Khadivi, S.: Using context vectors in improving a machine translation system with bridge language. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 318–322 (2013)

    Google Scholar 

  10. Hildebrand, A., Eck, M., Vogel, S., Waibel, A.: Adaptation of the translation model for statistical machine translation based on information retrieval. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2005)

    Google Scholar 

  11. Daume, H., Marcu, D.: Domain adaptation for statistical classifiers. J. Artif. Intell. (JAIR) 26, 101–126 (2006)

    Article  MathSciNet  Google Scholar 

  12. Koehn, P., Schroeder, J.: Experiments in domain adaptation for statistical machine translation. In: Proceedings of Workshop on Machine Translation (WMT) (2007)

    Google Scholar 

  13. Och, F.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL) (2003)

    Google Scholar 

  14. Wu, H., Wang, H.: Pivot language approach for phrase-based statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 856–863 (2007)

    Article  Google Scholar 

  15. Elkholy, A., Habash, N., Leusch, G., Matusov, E.: Selective combination of pivot and direct statistical machine translation models. In: Proceedings of the International Joint Conference on Natural Language Processing, Nagoya, Japan, pp. 1174–1180 (2013)

    Google Scholar 

  16. Koehn, P., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computer Linguistics (ACL), pp. 177–180 (2007)

    Google Scholar 

  17. Tiedemann, J.: Parallel data, tools and interfaces in OPUS. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC) (2012)

    Google Scholar 

  18. Pilevar, M., Faili, H., Pilevar, A.: TEP: Tehran English-Persian parallel corpus. In: Proceedings of 12th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing) (2011)

    Google Scholar 

  19. Heafield, K.: KenLM: faster and smaller language model queries. In: Proceedings of the 6th Workshop on Statistical Machine Translation, pp. 187–197 (2011)

    Google Scholar 

  20. Papineni, K., Roukos, S., Ward, T., Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL), pp. 311–318 (2002)

    Google Scholar 

Download references

Acknowledgment

The authors would like to express their sincere gratitude to Dr. Mojtaba Sabbagh-Jafari for his helpful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benyamin Ahmadnia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmadnia, B., Serrano, J., Haffari, G., Balouchzahi, NM. (2018). Direct-Bridge Combination Scenario for Persian-Spanish Low-Resource Statistical Machine Translation. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01204-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01203-8

  • Online ISBN: 978-3-030-01204-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics