Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment

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 concor-dancer 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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https://hal.archives-ouvertes.fr/hal-02021384
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Julien Bourdaillet, Stéphane Huet, Fabrizio Gotti, Guy Lapalme, Philippe Langlais. Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment. 22nd Conference of the Canadian Society for Computational Studies of Intelligence (Canadian AI 2009), May 2009, Kelowna, Canada. pp.27-38, ⟨10.1007/978-3-642-01818-3_6⟩. ⟨hal-02021384⟩

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