Skip to main content

Abstract

Achieving high-quality translation between any pair of languages is not possible with the current Machine-Translation (MT) technology a human post-editing of the outputs of the MT system being necessary. Therefore, MT is a suitable area to apply the Interactive Pattern Recognition (IPR) framework and this application has led to what nowadays is known as Interactive Machine Translation (IMT). IMT can predict the translation of a given source sentence, and the human translator can accept or correct some of the errors. The text amended by the human translator can be used by the system to suggest new improved translations with the same translation models in an iterative process until the whole output is accepted by the human.

As in other areas where IPR is being applied, IMT offers a nice framework for adaptive learning. The consolidated translations obtained through the successive steps of the interaction process can easily be converted into new, fresh, training data, useful for dynamically adapting the system to the changing environment. On the other hand, IMT also allows one to take advantage of some available multi-modal interfaces to increase of productivity. Multi-modal interfaces and adaptive learning in IMT will be covered in Chaps. 7 and 8, respectively.

With Contribution Of: Jorge Civera, Jesús González-Rubio and Daniel Ortiz-Martínez.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    Following the tradition in the error-correcting literature, it is assumed that the input data are a “distorted” version of the “correct” data represented by the model.

  2. 2.

    Although multiple reference translations would be desirable; because of the high cost of obtaining alternative reference translations only one reference translation is usually at our disposal.

  3. 3.

    The interested reader is referred to [7] for a detailed comparative of SMT evaluation measures.

References

  1. Amengual, J. C., & Vidal, E. (1998). Efficient error-correcting Viterbi parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(10), 1109–1116.

    Article  Google Scholar 

  2. Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Shahram, K., Lagarda, A. L., Ney, H., Tomás, J., Vidal, E., & Vilar, J. M. (2009). Statistical approaches to computer-assisted translation. Computational Linguistics, 35(8), 3–28.

    Article  MathSciNet  Google Scholar 

  3. Bender, O., Hasan, S., Vilar, D., Zens, R., & Ney, H. (2005). Comparison of generation strategies for interactive machine translation. In Proceedings of the 10th conference of the European chapter of the association for machine translation (EAMT 05) (pp. 33–40), Budapest, Hungary.

    Google Scholar 

  4. Blatz, J., Fitzgerald, E., Foster, G., Gandrabur, S., Goutte, C., Kuesza, A., Sanchis, A., & Ueffing, N. (2004). Confidence estimation for machine translation. In Proceedings of the 20th international conference on computational linguistics (COLING 04) (p. 315), Geneva, Switzerland.

    Chapter  Google Scholar 

  5. Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D., Mercer, R. L., & Roosin, P. S. (1990). A statistical approach to machine translation. Computational Linguistics, 16(2), 79–85.

    Google Scholar 

  6. Brown, P. F., Pietra, S. A. D., Pietra, V. J. D., & Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–310.

    Google Scholar 

  7. Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., & Schroeder, J. (2008). Further meta-evaluation of machine translation. In Proceedings of the 3rd workshop on statistical machine translation (WMT 08) (pp. 70–106), Morristown, NJ, USA.

    Chapter  Google Scholar 

  8. Callison-Burch, C., Koehn, P., Monz, C., Peterson, K., Przybocki, M., & Zaidan, O. (2010). Findings of the 2010 joint workshop on statistical machine translation and metrics for machine translation. In Proceedings of the joint fifth workshop on statistical machine translation and metricsMATR (pp. 17–53), Uppsala, Sweden.

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Casacuberta, F., Ney, H., Och, F. J., Vidal, E., Vilar, J., Barrachina, S., García-Varea, I., Llorens, D., Martínez, C., Molau, S., Nevado, F., Pastor, M., Picó, D., Sanchis, A., & Tillmann, C. (2004). Some approaches to statistical and finite-state speech-to-speech translation. Computer Speech & Language, 18, 25–47.

    Article  Google Scholar 

  11. Casacuberta, F., Civera, J., Cubel, E., Lagarda, A. L., Lapalme, G., Macklovitch, E., & Vidal, E. (2009). Human interaction for high quality machine translation. Communications of the ACM, 52(10), 135–138.

    Article  Google Scholar 

  12. Civera, J., Vilar, J. M., Cubel, E., Lagarda, A. L., Barrachina, S., Vidal, E., Casacuberta, F., Picó, D., & González, J. (2004). From machine translation to computer assisted translation using finite-state models. In Proceedings of the conference on empirical methods for natural language processing (EMNLP 04) (pp. 349–356), Barcelona, Spain.

    Google Scholar 

  13. Civera, J., Vilar, J. M., Cubel, E., Lagarda, A., Barrachina, S., Casacuberta, F., Vidal, E., Picó, D., & González, J. (2004). A syntactic pattern recognition approach to computer assisted translation. In A. Fred, T. Caelli, A. Campilho, R. P. W. Duin & D. de Ridder (Eds.), Lecture notes in computer science: Vol. 3138. Advances in statistical, structural and syntactical pattern recognition (pp. 207–215). Berlin: Springer.

    Chapter  Google Scholar 

  14. Cubel, E., González, J., Lagarda, A., Casacuberta, F., Juan, A., & Vidal, E. (2003). Adapting finite-state translation to the TransType2 project. In Proceedings of the joint conference combining the 8th international workshop of the European association for machine translation and the 4th controlled language applications workshop (EAMT-CLAW 03) (pp. 54–60), Dublin, Ireland.

    Google Scholar 

  15. Cubel, E., Civera, J., Vilar, J. M., Lagarda, A. L., Barrachina, S., Vidal, E., Casacuberta, F., Picó, D., González, J., & Rodríguez, L. (2004). Finite-state models for computer assisted translation. In Proceedings of the 16th European conference on artificial intelligence (ECAI 04) (pp. 586–590), Valencia, Spain.

    Google Scholar 

  16. Foster, G., Isabelle, P., & Plamondon, P. (1997). Target-text mediated interactive machine translation. Machine Translation, 12(1–2), 175–194.

    Article  Google Scholar 

  17. Foster, G., Langlais, P., & Lapalme, G. (2002). User-friendly text prediction for translators. In Proceedings of the conference on empirical methods in natural language processing (EMNLP 02) (pp. 148–155), Philadelphia, USA.

    Chapter  Google Scholar 

  18. Gandrabur, S., & Foster, G. (2003). Confidence estimation for text prediction. In Proceedings of the conference on natural language learning (CoNLL 03) (pp. 315–321), Edmonton, Canada.

    Google Scholar 

  19. González-Rubio, J., Ortiz-Martínez, D., & Casacuberta, F. (2010). Balancing user effort and translation error in interactive machine translation via confidence measures. In Proceedings of the 48th annual meeting of the association for computational linguistics (ACL 10) (pp. 173–177), Uppsala, Sweden.

    Google Scholar 

  20. González-Rubio, J., Ortiz-Martínez, D., & Casacuberta, F. (2010). On the use of confidence measures within an interactive-predictive machine translation system. In Proceedings of the 15th conference of the European chapter of the association for machine translation (EAMT 10), Saint-Raphaël, France.

    Google Scholar 

  21. Jiménez, V. M., & Marzal, A. (1999). Computing the k shortest paths: a new algorithm and an experimental coparison. In J. S. Viter & C. D. Zaraliagis (Eds.), Lecture notes in computer science: Vol. 1668. Algorithm engineering (pp. 15–29). Berlin: Springer.

    Chapter  Google Scholar 

  22. Koehn, P. (2010). Statistical machine translation. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  23. Koehn, P., & Haddow, B. (2009). Interactive assistance to human translators using statistical machine translation methods. In Proceedings of the 12th machine translation summit (MT Summit 09), Ottawa, Ontario, Canada.

    Google Scholar 

  24. 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. (2007). Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th annual meeting of the association for computational linguistics (ACL 07), Prague, Czech Republic.

    Google Scholar 

  25. Langlais, P., Foster, G., & Lapalme, G. (2000). Unit completion for a computer-aided translation typing system. Machine Translation, 15(4), 267–294.

    Article  MATH  Google Scholar 

  26. Langlais, P., Lapalme, G., & Loranger, M. (2002). TransType: development-evaluation cycles to boost translator’s productivity. Machine Translation, 15(4), 77–98.

    Article  Google Scholar 

  27. Lowerre, B. T. (1976). The Harpy speech recognition system. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA, USA.

    Google Scholar 

  28. Mariño, J. B., Banchs, R. E., Crego, J. M., de Gispert, A., Lambert, P., Fonollosa, J. A. R., & Costa-jussà, M. R. (2006). N-gram-based machine translation. Computational Linguistics, 32(4), 527–549.

    Article  MathSciNet  MATH  Google Scholar 

  29. Nepveu, L., Lapalme, G., Langlais, P., & Foster, G. (2004). Adaptive language and translation models for interactive machine translation. In Proceedings of the conference on empirical methods in natural language processing (EMNLP 04) (pp. 190–197), Barcelona, Spain.

    Google Scholar 

  30. Och, F. J. (2003). Minimum error rate training for statistical machine translation. In Proceedings of the 41st annual meeting of the association for computational linguistics (ACL 03) (pp. 160–167), Sapporo, Japan.

    Chapter  Google Scholar 

  31. Och, F. J., & Ney, H. (2004). The alignment template approach to statistical machine translation. Computational Linguistics, 30(4), 417–450.

    Article  MATH  Google Scholar 

  32. Och, F. J., Zens, R., & Ney, H. (2003). Efficient search for interactive statistical machine translation. In Proceedings of the 10th conference of the European chapter of the association for computational linguistics (EACL 03) (pp. 387–393), Budapest, Hungary.

    Google Scholar 

  33. Ortiz-Martínez, D., García-Varea, I., & Casacuberta, F. (2005). Thot: a toolkit to train phrase-based statistical translation models. In Proceedings of the 10th machine translation summit (MT Summit 05) (pp. 141–148), Phuket, Thailand.

    Google Scholar 

  34. Patry, A., & Langlais, P. (2009). Prediction of words in statistical machine translation using a multilayer perceptron. In Proceedings of the 12th machine translation summit (MT Summit 09) (pp. 101–111), Ottawa, Ontario, Canada.

    Google Scholar 

  35. Sanchis, A., Juan, A., & Vidal, E. (2007). Estimation of confidence measures for machine translation. In Proceedings of the 11th machine translation summit (MT Summit 07) (pp. 407–412), Copenhagen, Denmark.

    Google Scholar 

  36. SchlumbergerSema S.A., I. T. de Informática, für Informatik VI, R. W. T. H. A. L., en Linguistique Informatique Laboratory University of Montreal, R. A., Soluciones, C., Gamma, S., and Europe, X. R. C. (2001). TT2. TransType2—computer assisted translation. Project Technical Annex. Information Society Technologies (IST) Programme, IST-2001-32091.

    Google Scholar 

  37. Simard, M., & Isabelle, P. (2009). Phrase-based machine translation in a computer-assisted translation environment. In Proceedings of the 12th machine translation summit (MT Summit 09) (pp. 255–261), Ottawa, Ontario, Canada.

    Google Scholar 

  38. Tomás, J., & Casacuberta, F. (2006). Statistical phrase-based models for interactive computer-assisted translation. In Proceedings of the 44th annual meeting of the association for computational linguistics and 21th international conference on computational linguistics (COLING/ACL 06) (pp. 835–841), Sydney, Australia.

    Google Scholar 

  39. Ueffing, N., & Ney, H. (2005). Application of word-level confidence measures in interactive statistical machine translation. In Proceedings of the 10th conference of the European chapter of the association for machine translation (EAMT 05) (pp. 262–270), Budapest, Hungary.

    Google Scholar 

  40. Ueffing, N., & Ney, H. (2007). Word-level confidence estimation for machine translation. Computational Linguistics, 33(1), 9–40.

    Article  MATH  Google Scholar 

  41. Wagner, R. A. (1974). Order-n correction for regular languages. Communications of the ACM, 17(5), 265–268.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alejandro Héctor Toselli .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Toselli, A.H., Vidal, E., Casacuberta, F. (2011). Interactive Machine Translation. In: Multimodal Interactive Pattern Recognition and Applications. Springer, London. https://doi.org/10.1007/978-0-85729-479-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-479-1_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-478-4

  • Online ISBN: 978-0-85729-479-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics