Elamur Research⌕
☰
home / papers / 2009 / Statistical Approaches to Computer-Assisted Translation

Statistical Approaches to Computer-Assisted Translation

Summary

Machine translation output still needs human correction. The usual workflow, where MT runs first and a person post-edits afterwards, keeps the translator and the system apart. Barrachina et al. put the human inside the translation loop instead, an approach they call interactive-predictive machine translation (IPMT). The translator confirms or corrects the start of a target sentence, and the system proposes the best (or n-best) continuation. This repeats until the sentence is finished. Within the statistical framework, moving to this setting mainly means changing the search procedure, so existing models can be reused. Three model families were turned into IPMT engines in the European TransType2 (TT2) project: alignment templates (ATs), phrase-based models (PBMs) and stochastic finite-state transducers (SFSTs). They were evaluated on two real tasks, printer manuals and the Bulletin of the European Union. In simulated experiments, typing effort fell by up to roughly 80% compared with typing everything by hand. Response times were fast enough for real-time use. Field trials with professional translators showed productivity gains when the test texts resembled the training data.

Key points

  • Two tasks: Xerox printer manuals and the EU Bulletin. Each covered English–Spanish, English–German and English–French in both directions.
  • Unlike the original TransType, which predicted single tokens, the TT2 systems always suggest complete sentence continuations built on full SMT engines (AT, PBM, SFST).
  • On EU English→Spanish, PBM performed best. Its KSR went from 16.3% (1-best) to 13.2% (5-best), and its KSMR from 27.8% to 25.0%. AT and SFST scored around 19–21% KSR.
  • Offering 5-best suggestions clearly improved accuracy, but the benefit shrinks roughly logarithmically as n grows. The authors therefore suggest making n-best an optional feature.
  • Field tests showed little benefit when test texts were far from the training data. The authors also propose combining IPMT with translation memories to cover segments the memory cannot match.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Machine translation

Cited by

85 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 84 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.2008.07-055-r2-06-29

@article{barrachina2009statistical,
  title = {{Statistical Approaches to Computer-Assisted Translation}},
  author = {Barrachina, Sergio and Bender, Oliver and Casacuberta, Francisco and Civera, Jorge and Cubel, Elsa and Khadivi, Shahram and Lagarda, Antonio and Ney, Hermann and Tomás, Jesús and Vidal, Enrique and Vilar, Juan-Miguel},
  journal = {Computational Linguistics},
  year = {2009},
  doi = {10.1162/coli.2008.07-055-r2-06-29},
  url = {https://doi.org/10.1162/coli.2008.07-055-r2-06-29}
}

References

66 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Amengual, J. C., J. M. Benedı́, A. Castaño, A. Castellanos, V. M. Jiménez, D. Llorens, A. Marzal, M. Pastor, F. Prat, E. Vidal, and J. M. Vilar. 2000. The EuTrans-I speech translation system. Machine Translation, 15:75–103. find
  2. Amengual, J. C. and E. Vidal. 1998. Efficient error-correcting Viterbi parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(10):1109–1116. find
  3. Bender, O., S. Hasan, D. Vilar, R. Zens, and H. Ney. 2005. Comparison of generation strategies for interactive machine translation. In Proceedings of the 10th Annual Conference of the European Association for Machine Translation (EAMT 05), pages 33–40, Budapest. find
  4. Berger, A. L., P. F. Brown, S. A. Della Pietra, V. J. Della Pietra, J. R. Gillett, A. S. Kehler, and R. L. Mercer. 1996. Language translation apparatus and method of using context-based translation models. United States Patent No. 5510981, April. find
  5. Berstel, J. 1979. Transductions and Context-Free Languages. B. G. Teubner, Stuttgart. find
  6. Bisani, M. and H. Ney. 2004. Bootstrap estimates for confidence intervals in ASR performance evaluation. In Proceedings of the International Conference on Acoustic, Speech and Signal Processing (ICASSP 04), volume 1, pages 409–412, Montreal. find
  7. Bowker, L. 2002. Computer-Aided Translation Technology: A Practical Introduction, chapter 5: Translation-memory systems. Didactics of Translation. University of Ottawa Press, pages 92–127. find
  8. Brown, P. F., J. Cocke, S. A. Della Pietra, V. J. Della Pietra, F. Jelinek, J. D. Lafferty, R. L. Mercer, and P. S. Roosin. 1990. find
  9. A statistical approach to machine translation. Computational Linguistics, 16(2):79–85. find
  10. Brown, P. F., S. A. Della Pietra, V. J. Della Pietra, and R. L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263–310. find
  11. Callison-Burch, C., M. Osborne, and P. Koehn. 2006. Re-evaluating the role of BLEU in machine translation research. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL 06), pages 249–256, Trento. find
  12. Casacuberta, F., H. Ney, F. J. Och, E. Vidal, J. M. Vilar, S. Barrachina, I. Garcı́a-Varea, D. Llorens, C. Martı́nez, S. Molau, F. Nevado, M. Pastor, D. Picó, A. Sanchis, and C. Tillmann. 2004a. Some approaches to statistical and finite-state speech-to-speech translation. Computer Speech and Langua… find
  13. Casacuberta, F. and E. Vidal. 2004. Machine translation with inferred stochastic finite-state transducers. Computational Linguistics, 30(2):205–225. find
  14. Casacuberta, F. and E. Vidal. 2007. Learning finite-state models for machine translation. Machine Learning, 66(1):69–91. find
  15. Casacuberta, F., E. Vidal, and D. Picó. 2005. Inference of finite-state transducers from regular languages. Pattern Recognition, 38:1431–1443. find
  16. Casacuberta, F., E. Vidal, A. Sanchis, and J. M. Vilar. 2004b. Pattern recognition approaches for speech-to-speech translation. Cybernetic and Systems: an International Journal, 35(1):3–17. find
  17. Civera, J., J. M. Vilar, E. Cubel, A. L. Lagarda, S. Barrachina, E. Vidal, F. Casacuberta, D. Picó, and J. González. 2004a. From machine translation to computer assisted translation using finite-state models. In Proceedings of the Conference on Empirical Methods for Natural Language Processing (EM… find
  18. Civera, J., J. M. Vilar, E. Cubel, A. L. Lagarda, S. Barrachina, F. Casacuberta, E. Vidal, D. Picó, and J. González. 2004b. A syntactic pattern recognition approach to computer assisted translation. In Advances in Statistical, Structural and Syntactical Pattern Recognition, Proceedings of the Join… find
  19. Cubel, E., J. Civera, J. M. Vilar, A. L. Lagarda, S. Barrachina, E. Vidal, F. Casacuberta, D. Picó, J. González, and L. Rodrı́guez. 2004. Finite-state models for computer assisted translation. In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 04), pages 586–590, Valen… find
  20. Cubel, E., J. González, A. Lagarda, F. Casacuberta, A. Juan, and E. Vidal. 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 La… find
  21. Foster, G. 2002. Text Prediction for Translators. Ph.D. thesis, Université de Montréal, Canada. find
  22. Foster, G., P. Isabelle, and P. Plamondon. 1997. Target-text mediated interactive machine translation. Machine Translation, 12(1–2):175–194. find
  23. Isabelle, P. and K. Church. 1997. Special issue on new tools for human translators. Machine Translation, 12(1–2). find
  24. Jelinek, F. 1998. Statistical Methods for Speech Recognition. The MIT Press, Cambridge, MA. find
  25. Jiménez, V. M. and A. Marzal. 1999. Computing the k shortest paths: a new algorithm and an experimental comparison. In Algorithm Engineering: Proceedings of the 3rd International Workshop (WAE 99), London, UK, July 19–21, volume 1668 of Lecture Notes in Computer Science. Springer-Verlag, Heidelberg… find
  26. Kay, M. 1997. The proper place of men and machines in language translation. Machine Translation, 12:3–23. [This article first appeared as a Xerox PARC Working Paper in 1980]. find
  27. Khadivi, S. and C. Goutte. 2003. Tools for corpus alignment and evaluation of the alignments (deliverable d4.9). Technical report, TransType2 (IST-2001-32091). find
  28. Khadivi, S., R. Zens, and H. Ney. 2006. Integration of speech to computer-assisted translation using finite-state automata. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21th International Conference on Computational Linguistics (COLING/ACL 06), pages… find
  29. Khadivi, S., A. Zolnay, and H. Ney. 2005. Automatic text dictation in computer-assisted translation. In Proceedings of the European Conference on Speech Communication and Technology, (INTERSPEECH 05-EUROSPEECH), pages 2265–2268, Lisbon. find
  30. Koehn, P. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of the Conference on Empirical Methods for Natural Language Processing (EMNLP 04), pages 388–395, Barcelona. find
  31. Koehn, P., F. J. Och, and D. Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 03), pages 127–133, Edmonton. find
  32. Langlais, P., G. Foster, and G. Lapalme. 2000. TransType: a computer-aided translation typing system. In Proceedings of the NAACL/ANLP Workshop on Embedded Machine Translation Systems, pages 46–52, Seattle, WA. find
  33. Langlais, P., G. Lapalme, and M. Loranger. 2002. Transtype: Development-evaluation cycles to boost translator’s productivity. Machine Translation, 15(4):77–98. find
  34. Macklovitch, E. 2006. TransType2: The last word. In Proceedings of the 5th International Conference on Languages Resources and Evaluation (LREC 06), pages 167–172, Genoa. find
  35. Macklovitch, E., N. T. Nguyen, and R. Silva. 2005. User evaluation report. Technical report, TransType2 (IST-2001-32091). find
  36. Marcu, D. and W. Wong. 2002. A phrase-based, joint probability model for statistical machine translation. In Proceedings of the Conference on Empirical Methods for Natural Language Processing (EMNLP 02), pages 133–139, Philadelphia, PA. find our card
  37. Ney, H., S. Nießen, F. Och, H. Sawaf, C. Tillmann, and S. Vogel. 2000. Algorithms for statistical translation of spoken language. IEEE Transactions on Speech and Audio Processing, 8(1):24–36. find
  38. Och, F. J. 1999. An efficient method for determining bilingual word classes. In Proceedings of the 9th Conference of the European Chapter of the Association for Computational Linguistics (EACL 99), pages 71–76, Bergen. find our card
  39. Och, F. J. and H. Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1):19–51. find
  40. Och, F. J. and H. Ney. 2004. The alignment template approach to statistical machine translation. Computational Linguistics, 30(4):417–450. find
  41. Och, F. J., R. Zens, and H. Ney. 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), pages 387–393, Budapest. find
  42. Papineni, K., S. Roukos, T. Ward, and W. Zhu. 2001. BLEU: a method for automatic evaluation of machine translation. Technical Report RC22176, Thomas J. Watson Research Center. find our card
  43. Picó, D. and F. Casacuberta. 2001. Some statistical-estimation methods for stochastic finite-state transducers. Machine Learning, 44:121–142. find
  44. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. 2002. Numerical Recipes in C++: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK. find
  45. SchlumbergerSema S.A., Intituto Tecnológico de Informática, Rheinisch Westfälische Technische Hochschule Aachen Lehrstul für Informatik VI, Recherche Appliquée en Linguistique Informatique Laboratory University of Montreal, Celer Soluciones, Société Gamma, and Xerox Research Centre Europe. 20… find
  46. Sen, Z., Ch. Zhaoxiong, and H. Heyan. 1997. Interactive approach in machine translation systems. In Proceedings of IEEE International Conference on Intelligent Processing Systems (ICIPS 97), pages 1814–1819, Beijing. find
  47. Slocum, J. 1985. A survey of machine translation: Its history, current status and future prospects. Computational Linguistics, 11(1):1–17. find our card
  48. Somers, H., 2003. Computers and Translation: a Translator’s Guide, chapter 3: Translation memory systems. John Benjamins, Amsterdam, pages 31–48. find
  49. Tomás, J. and F. Casacuberta. 2001. Monotone statistical translation using word groups. In Proceedings of the Machine Translation Summit VIII (MT SUMMIT VIII), pages 357–361, Santiago de Compostela. find
  50. Tomás, J. and F. Casacuberta. 2003. Combining phrase-based and template-based alignment models in statistical translation. In Pattern Recognition and Image Analysis, Proceedings of the First Iberian Conference (IbPRIA 03), Puerto de Andratx, Mallorca, Spain, June 4-6, volume 2652 of Lecture Notes i… find
  51. Tomás, J. and F. Casacuberta. 2004. Statistical machine translation decoding using target word reordering. In Advances in Statistical, Structural and Syntactical Pattern Recognition, Proceedings of the Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 04) an… find
  52. Tomás, J. and F. Casacuberta. 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), pages 835–841… find
  53. Tomás, J. and F. Casacuberta. 2007. A pattern recognition approach to machine translation: Monotone and non-monotone phrase-based statistical models. Technical Report DSIC-II/18/07, Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia. find
  54. Tomita, M. 1985. Feasibility study of personal/interactive machine translation systems. In Proceedings of the First International Conference on Theoretical and Methodological Issues in Machine Translation (TMI 85), pages 289–297, New York, NY. find
  55. Ueffing, N., F. J. Och, and H. Ney. 2002. Generation of word graphs in statistical machine translation. In Proceedings of the Conference on Empirical Methods for Natural Language Processing (EMNLP 02), pages 156–163, Philadelphia, PA. find
  56. Vidal, E. 1997. Finite-state speech-to-speech translation. In Proceedings of the International Conference on Acoustic, Speech and Signal Processing (ICASSP 97), volume 1, pages 111–114, Munich. find
  57. Vidal, E. and F. Casacuberta. 2004. Learning finite-state models for machine translation. In Grammatical Inference: Algorithms and Applications, Proceedings of the 7th International Coloquium on Grammatical Inference (ICGI 04), Athens, Greece, October 11–13, volume 3264 of Lecture Notes in Artificia… find
  58. Vidal, E., F. Casacuberta, L. Rodrı́guez, J. Civera, and C. Martı́nez. 2006. Computer-assisted translation using speech recognition. IEEE Transactions on Speech and Audio Processing, 14(3):941–951. find
  59. Vidal, E., F. Thollard, F. Casacuberta C. de la Higuera, and R. Carrasco. 2005. Probabilistic finite-state machines— part II. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7):1025–1039. find
  60. Whitelock, P. J., M. McGee Wood, B. J. Chandler, N. Holden, and H. J. Horsfall. 1986. Strategies for interactive machine translation: The experience and implications of the UMIST Japanese project. In Proceedings of the 11th International Conference on Computational Linguistics (COLING 86), pages 329… find
  61. Yamron, J., J. Baker, P. Bamberg, H. Chevalier, T. Dietzel, J. Elder, F. Kampmann, M. Mandel, L. Manganaro, T. Margolis, and E. Steele. 1993. find
  62. LINGSTAT: an interactive, machine-aided translation system. In Proceedings of the Workshop on Human Language Technology, pages 191–195, Princeton, NJ. find
  63. Zajac, R. 1988. Interactive translation: A new approach. In Proceedings of the 12th International Conference on Computational Linguistics (COLING 88), pages 785–790, Budapest. find
  64. Zens, R. and H. Ney. 2004. Improvements in phrase-based statistical machine translation. In Proceedings of the Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL 04), pages 257–264, Boston, MA. find our card
  65. Zens, R., F. J. Och, and H. Ney. 2002. Phrase-based statistical machine translation. In Advances in Artificial Intelligence. 25th Annual German Conference on Artificial Intelligence (KI 02), Aachen, Germany, September 16–22, Proceedings, volume 2479 of Lecture Notes on Artificial Intelligence. Sprin… find
  66. Zhang, Y. and S. Vogel. 2004. Measuring confidence intervals for the machine translation evaluation metrics. In Proceedings of the Tenth International Conference on Theoretical and Methodological Issues in Machine Translation (TMI 04), pages 294–301, Baltimore, MD. find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/barrachina-2009-statistical-approaches-computer-assisted-translation/