Abstract
Despite the impressive amount of recent studies devoted to improving the state of the art of Machine Translation (MT), Computer Assisted Translation (CAT) tools remain the preferred solution of human translators when publication quality is of concern. In this paper, we present our perspectives on improving the commercial bilingual concordancer TransSearch, a Web-based service whose core technology mainly relies on sentence-level alignment. We report on experiments which show that it can greatly benefit from statistical word-level alignment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Macklovitch, E., Lapalme, G., Gotti, F.: Transsearch: What are translators looking for? In: 18th Conference of the Association for Machine Translation in the Americas (AMTA), Waikiki, Hawai’i, USA, pp. 412–419 (2008)
Simard, M., Macklovitch, E.: Studying the human translation process through the TransSearch log-files. In: AAAI Symposium on Knowledge Collection from volunteer contributor, Stanford, CA, USA (2005)
Véronis, J., Langlais, P.: 19. In: Evaluation of Parallel text Alignment Systems — The Arcade Project, pp. 369–388. Kluwer Academic Publisher, Dordrecht (2000)
Simard, M.: Translation spotting for translation memories. In: HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond, Edmonton, Canada, pp. 65–72 (2003)
Brown, P., Della Pietra, V., Della Pietra, S., Mercer, R.: The mathematics of statistical machine translation: parameter estimation. Computational Linguistics 19(2), 263–311 (1993)
Freund, Y., Schapire, R.: Large margin classification using the perceptron algorithm. Machine Learning 37(3), 277–296 (1999)
Collins, M.: Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In: EMNLP 2002, Philadelphia, PA, USA, pp. 1–8 (2002)
Liang, P., Bouchard-Côté, A., Klein, D., Taskar, B.: An end-to-end discriminative approach to machine translation. In: 21st COLING and 44th ACL, Sydney, Australia, pp. 761–768 (2006)
Chenna, R., Sugawara, H., Koike, T., Lopez, R., Gibson, T.J., Higgins, D.G., Thompson, J.D.: Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Research 31(13), 3497–3500 (2003)
Saiou, N., Nei, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4(4), 406–425 (1987)
Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003)
Fleiss, J.L., Levin, B., Pai, M.C.: Statistical Methods for Rates and Proportions, 3rd edn. Wiley Interscience, Hoboken (2003)
Callisson-Burch, C., Bannard, C., Schroeder, J.: A compact data structure for searchable translation memories. In: 10th European Conference of the Association for Machine Translation (EAMT), Budapest, Hungary, pp. 59–65 (2005)
Vogel, S., Ney, H., Tillmann, C.: HMM-based word alignment in statistical translation. In: 16th conference on Computational linguistics, Copenhagen, Denmark, pp. 836–841 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bourdaillet, J., Huet, S., Gotti, F., Lapalme, G., Langlais, P. (2009). Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-01818-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01817-6
Online ISBN: 978-3-642-01818-3
eBook Packages: Computer ScienceComputer Science (R0)