Abstract
We propose a new post-editing method for statistical machine translation. The method acquires translation rules automatically as translation knowledge from a parallel corpus without depending on linguistic tools. The translation rules, which are acquired based on Intuitive Common Parts Continuum (ICPC), can deal with the correspondence of the global structure of a source sentence and that of a target sentence without requiring linguistic tools. Moreover, it generates better translation results by application of translation rules to translation results obtained through statistical machine translation. The experimentally obtained results underscore the effectiveness of applying the translation rules for statistical machine translation.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Brown, P.F., Cocke, J., Pietra, S.A.D., Pietra, V.J.D., Jelinek, F., Lafferty, J.D., Mercer, R.L., Roosin, P.S.: A Statistical Approach to Machine Translation. Computational Linguistics 16(2), 79–85 (1990)
Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., Mercer, R.L.: The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19(2), 263–311 (1993)
Koehn, P., Och, F.J., Marcu, D.: Statistical Phrase-based Translation. In: Proc. of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 48–54 (2003)
Chiang, D.: A Hierarchical Phrase-based Model for Statistical Machine Translation. In: Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 263–270 (2005)
McDonald, R., Crammer, K., Pereira, F.: Online Large-Margin Training of Dependency Parsers. In: Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 91–98 (2005)
Chiang, D., Marton, Y., Resnik, P.: Online Large-Margin Training of Syntactic and Structural Translation Features. In: Proc. of the Conference on Empirical Methods in Natural Language Processing, pp. 224–233 (2008)
Cherry, C., Moore, R.C., Quirk, C.: On Hierarchical Re-ordering and Permutation Parsing for Phrase-based Decoding. In: Proc. of the Seventh Workshop on Statistical Machine Translation, pp. 200–209 (2012)
Dugast, L., Senellart, J., Koehn, P.: Statistical Post-Editing on SYSTRAN’s Rule-Based Translation System. In: Proc. of the Second Workshop on Statistical Machine Translation, pp. 220–223 (2007)
Plitt, M., Masselot, F.: A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context. The Prague Bulletin of Mathematical Linguistics 93, 7–16 (2010)
Echizen-ya, H., Araki, K.: Automatic Evaluation of Machine Translation based on Recursive Acquisition of an Intuitive Common Parts Continuum. In: Proc. of the Eleventh Machine Translation Summit, pp. 151–158 (2007)
Echizen’ya, H., Araki, K., Hovy, E.: Optimization for Efficient Determination of Chunk in Automatic Evaluation for Machine Translation. In: Proc. of the 1st International Workshop on Optimization Techniques for Human Language Technology (OPTHLT 2012) / COLING 2012, pp. 17–30 (2012)
Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: A Method for Automatic Evaluation of Machine Translation. In: Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Doddington, G.: Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics. In: Proc. of the second International Conference on Human Language Technology Research, pp. 138–145 (2002)
Echizen’ya, H., Araki, K., Hovy, E.: Application of Prize based on Sentence Length in Chunk-based Automatic Evaluation of Machine Translation. In: Proc. of the Ninth Workshop on Statistical Machine Translation, pp. 381–386 (2014)
Och, F.J., Ney, H.: A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics 29(1), 19–51 (2003)
Stolcke, A.: SRILM – An Extensible Language Modeling Toolkit. In: Proc. of the International Conference on Spoken Language Processing, pp. 901–904 (2002)
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: Proc. of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180 (2007)
Isozaki, H., Sudoh, K., Tsukada, H., Duh, K.: Head Finalization: A Simple Reordering Rule for SOV Languages. In: Proc. of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, pp. 244–251 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Echizen’ya, H., Araki, K., Uchida, Y., Hovy, E. (2014). Automatic Post-Editing Method Using Translation Knowledge Based on Intuitive Common Parts Continuum for Statistical Machine Translation. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-11581-8_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11580-1
Online ISBN: 978-3-319-11581-8
eBook Packages: Computer ScienceComputer Science (R0)