Skip to main content

Improving the Minimum Description Length Inference of Phrase-Based Translation Models

  • Conference paper
  • First Online:
Pattern Recognition and Image Analysis (IbPRIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9117))

Included in the following conference series:

  • 4003 Accesses

Abstract

We study the application of minimum description length (MDL) inference to estimate pattern recognition models for machine translation. MDL is a theoretically-sound approach whose empirical results are however below those of the state-of-the-art pipeline of training heuristics. We identify potential limitations of current MDL procedures and provide a practical approach to overcome them. Empirical results support the soundness of the proposed approach.

J. González-Rubio—This author is now at Unbabel Lda. 1000-201 Lisboa, Portugal.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    We have 8 different symbols: one symbol for each of the six words, plus \({{\mathrm{\bullet }}}\) and \({{\mathrm{\vert }}}\).

  2. 2.

    14 is the maximum phrase pair length usually considered by conventional PB systems.

References

  1. Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 48–54 (2003)

    Google Scholar 

  2. Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the Association for Computational Linguistics, Demonstration Session (2007)

    Google Scholar 

  3. Sanchis-Trilles, G., Ortiz-Martínez, D., González-Rubio, J., González, J., Casacuberta, F.: Bilingual segmentation for phrasetable pruning in statistical machine translation. In: Proceedings of the Conference of the European Association for Machine Translation (2011)

    Google Scholar 

  4. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  5. González-Rubio, J., Casacuberta, F.: Inference of phrase-based translation models via minimum description length. In: Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, pp. 90–94 (2014)

    Google Scholar 

  6. Marcu, D., Wong, W.: A phrase-based, joint probability model for statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 133–139 (2002)

    Google Scholar 

  7. DeNero, J., Bouchard-Côté, A., Klein, D.: Sampling alignment structure under a bayesian translation model. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 314–323 (2008)

    Google Scholar 

  8. Zhang, J.: Model-based search for statistical machine translation. Master’s thesis, Edinburgh University, United Kingdom (2005)

    Google Scholar 

  9. Vilar, J.M., Vidal, E.: A recursive statistical translation model. In: Proceedings of the ACL Workshop on Building and Using Parallel Texts, pp. 199–207 (2005)

    Google Scholar 

  10. Shannon, C.: A mathematical theory of communication. Bell Syst. Techn. J. 27, 379–423/623–656 (1948)

    Google Scholar 

  11. Grünwald, P.: A tutorial introduction to the minimum description length principle (2004). http://arxiv.org/abs/math/0406077

  12. Saers, M., Addanki, K., Wu, D.: Iterative rule segmentation under minimum description length for unsupervised transduction grammar induction. In: Dediu, A.-H., Martín-Vide, C., Mitkov, R., Truthe, B. (eds.) SLSP 2013. LNCS, vol. 7978, pp. 224–235. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Zipf, G.K.: The Psychobiology of Language. Houghton-Mifflin, Boston (1935)

    Google Scholar 

  14. Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., Schroeder, J.: (Meta-) evaluation of machine translation. In: Proceedings of the Workshop on Statistical Machine Translation, pp. 136–158 (2007)

    Google Scholar 

  15. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: A method for automatic evaluation of machine translation. In: Proceedings of the Meeting on Association for Computational Linguistics, Association for Computational Linguistics, pp. 311–318 (2002)

    Google Scholar 

  16. Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of Association for Machine Translation in the Americas, pp. 223–231 (2006)

    Google Scholar 

  17. Turchi, M., De Bie, T., Cristianini, N.: Learning to translate: a statistical and computational analysis. Technical report, University of Bristol (2009)

    Google Scholar 

Download references

Acknowledgments

Work supported by the EU \(7^\mathrm{th}\) Framework Programme (FP7/2007–2013) under the CasMaCat project (grant agreement n\(^{\text{ o }}\) 287576), by Spanish MICINN under grant TIN2012-31723, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jesús González-Rubio .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

González-Rubio, J., Casacuberta, F. (2015). Improving the Minimum Description Length Inference of Phrase-Based Translation Models. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19390-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19389-2

  • Online ISBN: 978-3-319-19390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics