Abstract
This paper presents an idea in Example-Based Machine Translation - computing the transfer score for each produced translation. When an EBMT system finds an example in the translation memory, it tries to modify the sentence in order to produce the best possible translation of the input sentence. The user of the system, however, is unable to judge the quality of the translation. This problem can be solved by providing the user with a percentage score for each translated sentence.
The idea to base transfer score computation on the similarity between the input sentence and the example is not sufficient. Real-life examples show that the transfer process is as likely to go well with a bad translation memory example as to fail with a good example.
This paper describes a method of computing transfer score strictly associated with the transfer process. The transfer score is inversely proportional to the number of linguistic operations executed on the example target sentence. The paper ends with an evaluation of the suggested method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Somers, H., Dandapat, S., Naskar, S.K.: A review of ebmt using proportional analogies. In: Proceedings of the 3rd International Workshop on Example-Based Machine Translation (2009)
Vandeghinste, V., Martens, S.: Top-down transfer in example-based mt. In: Proceedings of the 3rd International Workshop on Example-Based Machine Translation (2009)
Kurohashi, S.: Fully syntactic example-based machine translation. In: Proceedings of the 3rd International Workshop on Example-Based Machine Translation (2009)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation (2002), http://www1.cs.columbia.edu/nlp/sgd/bleu.pdf
Hongxu, H., Dan, D., Gang, Z., Hongkui, Y., Yang, L., Deyi, X.: An EBMT system based on word alignment (2004), http://www.mt-archive.info/IWSLT-2004-Hou.pdf
Rapp, R., Vide, C.M.: Example-based machine translation using a dictionary of word pairs (2006), http://www.mt-archive.info/LREC-2006-Rapp.pdf
Jassem, K., Marcińczuk, M.: Semi-supervised learning rule acquisition for Named Entity recognition and translation (2008) (unpublished)
Gintrowicz, J.: Tłumaczenie automatyczne oparte na przykładach i jego rozszerzenia. Master thesis under the supervision of dr Krzysztof Jassem (2007)
Lavie, A., Agarwal, A., Denkowski, M.: The meteor metric for automatic evaluation of machine translation (2009), http://www.cs.cmu.edu/~alavie/METEOR/meteor-mtj-2009.pdf
Ralf, S., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufiş, D., Varga, D.: The jrc-acquis: A multilingual aligned parallel corpus with 20+ languages. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (2006)
Stigler, M.S.: Francis galton’s account of the invention of correlation. Statistical Science (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaworski, R. (2010). Computing Transfer Score in Example-Based Machine Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-12116-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12115-9
Online ISBN: 978-3-642-12116-6
eBook Packages: Computer ScienceComputer Science (R0)