Abstract
High-quality translation between any pair of languages can be achieved by human post-editing of the outputs of a MT system or, as mentioned in Chap. 6, by following the Interactive Machine Translation (IMT) approach. In the interactive pattern recognition framework, IMT can predict the translation of the next words in the output, and can suggest them to the human translator who, iteratively, can accept or correct the suggested translations. The consolidated translations obtained through the successive steps of the interaction process can be considered as “perfect translations” due to the fact that they have been validated by a human expert. Therefore, this consolidated translations can easily be converted into new, fresh, training data, useful for dynamically adapting the system to the changing environment. Taking that into account, on the one hand, the IMT paradigm offers an appropriate framework for incremental and adaptive learning in SMT. On the other hand, incremental and adaptive learning offers the possibility to substantially save human effort by simply avoiding the user to perform the same corrections again and again.
With Contribution Of: Daniel Ortiz-Martínez and Ismael García-Varea.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arun, A., & Koehn, P. (2007). Online learning methods for discriminative training of phrase based statistical machine translation. In Proceedings of the machine translation summit XII (MT Summit 07) (pp. 15–20), Copenhagen, Denmark.
Bertoldi, N., & Federico, M. (2009). Domain adaptation for statistical machine translation with monolingual resources. In Proceedings of the EACL 09 fourth workshop on statistical machine translation (WSMT 09) (pp. 182–189), Athens, Greece.
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.
Callison-burch, C., Bannard, C., & Schroeder, J. (2004). Improving statistical translation through editing. In Proceedings of the 9th EAMT workshop broadening horizons of machine translation and its applications, Malta.
Cesa-Bianchi, N., Reverberi, G., & Szedmak, S. (2008). Online learning algorithms for computer-assisted translation. Deliverable D4.2, SMART: Statistical Multilingual Analysis for Retrieval and Translation.
Chiang, D., Marton, Y., & Resnik, P. (2008). Online large-margin training of syntactic and structural translation features. In Proceedings of the conference on empirical methods in natural language processing (EMNLP 08) (pp. 224–233), Honolulu, Hawaii.
Civera, J., et al. (2004). A syntactic pattern recognition approach to computer assisted translation. In A. Fred, T. Caelli, A. Campilho, R. P. Duin, & D. de Ridder (Eds.), Lecture notes in computer science. Advances in statistical, structural and syntactical pattern recognition (pp. 207–215). Berlin: Springer.
Knuth, D. E. (1981). Seminumerical algorithms: Vol. 2. The art of computer programming (2nd ed.). Reading: Addison-Wesley.
Koehn, P., & Schroeder, J. (2007). Experiments in domain adaptation for statistical machine translation. In Proceedings of the ACL 2007 second workshop on statistical machine translation (WSMT 07) (pp. 224–227), Prague, Czech Republic.
Koehn, P., Och, F. J., & Marcu, D. (2003). Statistical phrase-based translation. In Proceedings of the human language technology and North American association for computational linguistics conference (HLT/NAACL 03) (pp. 48–54), Edmonton, Canada.
Liang, P., Bouchard-Côté, A., Klein, D., & Taskar, B. (2006). An end-to-end discriminative approach to machine translation. In Proceedings of the joint international conference on computational linguistics and association of computational linguistics (COLING/ACL 06) (pp. 761–768), Sydney, Australia.
Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models (pp. 355–368). Dordrecht: Kluwer Academic.
Nepveu, L., Lapalme, G., Langlais, P., & Foster, G. F. (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.
Ortiz-Martínez, D., García-Varea, I., & Casacuberta, F. (2008). The scaling problem in the pattern recognition approach to machine translation. Pattern Recognition Letters, 29, 1145–1153.
Ortiz-Martínez, D., García-Varea, I., & Casacuberta, F. (2009). Interactive machine translation based on partial statistical phrase-based alignments. In Proceedings of the international conference recent advances in natural language processing (RANLP 09) (pp. 330–336), Borovets, Bulgaria.
Ortiz-Martínez, D., García-Varea, I., & Casacuberta, F. (2010). Online learning for interactive statistical machine translation. In Proceedings of the 11th annual conference of the North American chapter of the association for computational linguistics (NAACL 10) (pp. 546–554), Los Angeles, USA.
Sanchis, G., & Casacuberta, F. (2010). Bayesian adaptation for statistical machine translation. In Proceedings of the 23rd international conference on computational linguistics (COLING 10) (pp. 1077–1085), Beijing, China.
Sanchis-Trilles, G., & Cettolo, M. (2010). Online language model adaptation via n-gram mixtures for statistical machine translation. In Proceedings of the conference of the European association for machine translation (EAMT 10), Saint-Raphaël, France.
Vogel, S., Ney, H., & Tillmann, C. (1996). HMM-based word alignment in statistical translation. In Proceedings of the 16th international conference on computational linguistics (COLING 96) (pp. 836–841), Copenhagen, Denmark.
Watanabe, T., Suzuki, J., Tsukada, H., & Isozaki, H. (2007). Online large-margin training for statistical machine translation. In Proceedings of the joint conference on empirical methods in natural language processing (EMNLP 07) and computational natural language learning (CoNLL 07) (pp. 764–773), Prague, Czech Republic.
Zhao, B., Eck, M., & Vogel, S. (2004). Language model adaptation for statistical machine translation with structured query models. In Proceedings of the international conference on computational linguistics (COLING 04) (pp. 411–417), Geneva, Switzerland.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag London Limited
About this chapter
Cite this chapter
Toselli, A.H., Vidal, E., Casacuberta, F. (2011). Incremental and Adaptive Learning for Interactive Machine Translation. In: Multimodal Interactive Pattern Recognition and Applications. Springer, London. https://doi.org/10.1007/978-0-85729-479-1_8
Download citation
DOI: https://doi.org/10.1007/978-0-85729-479-1_8
Publisher Name: Springer, London
Print ISBN: 978-0-85729-478-4
Online ISBN: 978-0-85729-479-1
eBook Packages: Computer ScienceComputer Science (R0)