Skip to main content
Springer Nature Link
Log in

Learning Finite State Transducers Using Bilingual Phrases

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4919))

  • 1512 Accesses

Abstract

Statistical Machine Translation is receiving more and more attention every day due to the success that the phrase-based alignment models are obtaining. However, despite their power, state-of-the-art systems using these models present a series of disadvantages that lessen their effectiveness in working environments where temporal or spacial computational resources are limited. A finite-state framework represents an interesting alternative because it constitutes an efficient paradigm where quality and realtime factors are properly integrated in order to build translation devices that may be of help for their potential users. Here, we describe a way to use the bilingual information in a phrase-based model in order to implement a phrase-based ngram model using finite state transducers. It will be worth the trouble due to the notable decrease in computational requirements that finite state transducers present in practice with respect to the use of some well-known stack-decoding algorithms. Results for the French-English EuroParl benchmark corpus from the 2006 Workshop on Machine Translation of the ACL are reported.

This work has been partially supported by the EC (FEDER) and the Spanish projects TIN2006-15694-C02-01 and the Consolider Ingenio 2010 CSD2007-00018.

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

Access this chapter

Log in via an institution

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Brown, P.F., et al.: A statistical approach to machine translation. Computational Linguistics 16(2), 79–85 (1990)

    Google Scholar 

  2. Brown, P.F., et al.: The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19(2), 263–311 (1993)

    Google Scholar 

  3. Ney, H., et al.: Algorithms for statistical translation of spoken language. IEEE Transactions on Speech and Audio Processing 8(1), 24–36 (2000)

    Article  Google Scholar 

  4. Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003)

    Article  Google Scholar 

  5. Tomás, J., Casacuberta, F.: Monotone statistical translation using word groups. In: Proceedings of the Machine Translation Summit VIII, Santiago de Compostela, Spain, pp. 357–361 (2001)

    Google Scholar 

  6. Zens, R., Och, F.J., Ney, H.: Phrase-based statistical machine translation. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 18–32. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Casacuberta, F., et al.: Some approaches to statistical and finite-state speech-to-speech translation. Computer Speech and Language 18, 25–47 (1994)

    Article  Google Scholar 

  8. Casacuberta, F., Vidal, E.: Machine translation with inferred stochastic finite-state transducers. Computational Linguistics 30(2), 205–225 (2004)

    Article  MathSciNet  Google Scholar 

  9. Casacuberta, F., Vidal, E., Picó, D.: Inference of finite-state transducers from regular languages. Pattern Recognition 38(9), 1431–1443 (2005)

    Article  MATH  Google Scholar 

  10. Berger, A.L., et al.: Language Translation apparatus and method of using context-based translation models. United States Patent, No. 5510981 (1996)

    Google Scholar 

  11. Ortiz, D., García-Varea, I., Casacuberta, F.: An empirical comparison of stack-based decoding algorithms for statistical machine translation. In: New Advance in Computer Vision. LNCS, Springer, Heidelberg (2003)

    Google Scholar 

  12. Germann, U., et al.: Fast Decoding and Optimal Decoding for Machine Translation. In: ACL 2001, Toulouse, France, pp. 228–235 (2001)

    Google Scholar 

  13. Llorens, D.: Suavizado de autómatas y traductores finitos estocásticos. Phd Thesis, Universidad Politécnica de Valencia (2000)

    Google Scholar 

  14. Kumar, S., Deng, Y., Byrne, W.: A weighted finite state transducer translation template model for statistical machine translation. Natural Language Engineering 12(1), 35–75 (2006)

    Article  Google Scholar 

  15. Koehn, P., Monz, C.: Manual and Automatic Evaluation of Machine Translation between European Languages. In: NAACL 2006 Workshop on Statistical Machine Translation, pp. 102–121 (2006)

    Google Scholar 

  16. Koehn, P., Och, F.J., Marcu, D.: Statistical Phrase-Based Translation’. In: NAACL/HLT 2003. In: Proceedings of the Human Language Technology and North American Association for Computational Linguistics Conference, May 27–June 1 2003, Edmonton, Canada,(2003)

    Google Scholar 

  17. Koehn, P.: Europarl: A Parallel Corpus for Statistical Machine Translation. In: Proceedings of the 10th Machine Translation Summit, pp. 79–86 (2005)

    Google Scholar 

  18. Papineni, A.K., et al.: Bleu: A method for automatic evaluation of machine translation. In Technical Report RC22176 (W0109-022), IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY (2001)

    Google Scholar 

  19. Koehn, P., et al.: Moses: Open Source Toolkit for Statistical Machine Translation. Annual Meeting of the Association for Computational Linguistics (demonstration session) (2007)

    Google Scholar 

  20. González, J., Casacuberta, F.: Phrase-based finite state models. In: Proceedings of the 6th International Workshop on Finite-State Methods and Natural Language Processing (2007)

    Google Scholar 

  21. Casacuberta, F.: Inference of finite-state transducers by using regular grammars and morphisms. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 1–14. Springer, Heidelberg (2000)

    Google Scholar 

  22. Mariño, J.B., et al.: N-gram-based Machine Translation. Computational Linguistics 32(4), 527–549 (2006)

    Article  MathSciNet  Google Scholar 

  23. Ortiz, D., García-Varea, I., Casacuberta, F.: Thot: a Toolkit To Train Phrase-based Statistical Translation Models. In: Proceedings of the 10th Machine Translation Summit, pp. 141–148 (2005)

    Google Scholar 

  24. Casacuberta, F., et al.: Human Interaction for high quality machine translation. In: Communications of the ACM (in press, 2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia,  

    Jorge González, Germán Sanchis & Francisco Casacuberta

Authors
  1. Jorge González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Germán Sanchis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Casacuberta
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Alexander Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

González, J., Sanchis, G., Casacuberta, F. (2008). Learning Finite State Transducers Using Bilingual Phrases. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78135-6_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78134-9

  • Online ISBN: 978-3-540-78135-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics