Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment

Authors:
Julien Bourdaillet;Stéphane Huet;Fabrizio Gotti;Guy Lapalme;Philippe Langlais
Affiliations:
Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada H3C 3J7;Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada H3C 3J7;Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada H3C 3J7;Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada H3C 3J7;Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada H3C 3J7
Venue:
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2009

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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.