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.
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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
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DOI: https://doi.org/10.1007/978-3-642-01818-3_6
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