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Using inter-lingual triggers for Machine translation

Identifieur interne : 004770 ( Main/Exploration ); précédent : 004769; suivant : 004771

Using inter-lingual triggers for Machine translation

Auteurs : Caroline Lavecchia [France] ; Kamel Smaïli [France] ; David Langlois [France] ; Jean-Paul Haton [France]

Source :

RBID : Hal:inria-00155791

Abstract

In this paper, we present the idea of cross-lingual triggers. We exploit this formalism in order to build up a bilingual dictionary for machine translation. We describe the idea of cross-lingual triggers, the way to exploit and to make good use of them in order to produce a bilingual dictionary. We then compare it to ELRA and a free downloaded dictionaries. Finally, our dictionary is evaluated by comparing it to the one achieved by GIZA++ (which is an extension of the program GIZA) into an entire translation decoding process supplied by Pharaoh. The experiments showed that the obtained dictionary is well constructed and is suitable for machine translation. The experiments have been conducted on a parallel corpus of 19 million French words and of 17 million English words. Finally, the encouraging results allow us to put forward the concept of cross-lingual triggers which could have so many applications in machine translation.

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Le document en format XML

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<div type="abstract" xml:lang="en">In this paper, we present the idea of cross-lingual triggers. We exploit this formalism in order to build up a bilingual dictionary for machine translation. We describe the idea of cross-lingual triggers, the way to exploit and to make good use of them in order to produce a bilingual dictionary. We then compare it to ELRA and a free downloaded dictionaries. Finally, our dictionary is evaluated by comparing it to the one achieved by GIZA++ (which is an extension of the program GIZA) into an entire translation decoding process supplied by Pharaoh. The experiments showed that the obtained dictionary is well constructed and is suitable for machine translation. The experiments have been conducted on a parallel corpus of 19 million French words and of 17 million English words. Finally, the encouraging results allow us to put forward the concept of cross-lingual triggers which could have so many applications in machine translation.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Nancy</li>
</settlement>
<orgName>
<li>Institut national polytechnique de Lorraine</li>
<li>Université Nancy 2</li>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Grand Est">
<name sortKey="Lavecchia, Caroline" sort="Lavecchia, Caroline" uniqKey="Lavecchia C" first="Caroline" last="Lavecchia">Caroline Lavecchia</name>
</region>
<name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name>
<name sortKey="Langlois, David" sort="Langlois, David" uniqKey="Langlois D" first="David" last="Langlois">David Langlois</name>
<name sortKey="Smaili, Kamel" sort="Smaili, Kamel" uniqKey="Smaili K" first="Kamel" last="Smaïli">Kamel Smaïli</name>
</country>
</tree>
</affiliations>
</record>

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