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Building and using multimodal comparable corpora for machine translation†

Published online by Cambridge University Press:  15 June 2016

HAITHEM AFLI ,
LOÏC BARRAULT  and
HOLGER SCHWENK
HAITHEM AFLI
Affiliation:
Université du Maine, Avenue Olivier Messiaen F-72085 - LE MANS, France e-mails: haithem.afli@lium.univ-lemans.fr, loic.barrault@lium.univ-lemans.fr, holger.schwenk@lium.univ-lemans.fr
LOÏC BARRAULT
Affiliation:
Université du Maine, Avenue Olivier Messiaen F-72085 - LE MANS, France e-mails: haithem.afli@lium.univ-lemans.fr, loic.barrault@lium.univ-lemans.fr, holger.schwenk@lium.univ-lemans.fr
HOLGER SCHWENK
Affiliation:
Université du Maine, Avenue Olivier Messiaen F-72085 - LE MANS, France e-mails: haithem.afli@lium.univ-lemans.fr, loic.barrault@lium.univ-lemans.fr, holger.schwenk@lium.univ-lemans.fr
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Abstract

In recent decades, statistical approaches have significantly advanced the development of machine translation systems. However, the applicability of these methods directly depends on the availability of very large quantities of parallel data. Recent works have demonstrated that a comparable corpus can compensate for the shortage of parallel corpora. In this paper, we propose an alternative to comparable corpora containing text documents as resources for extracting parallel data: a multimodal comparable corpus with audio documents in source language and text document in target language, built from Euronews and TED web sites. The audio is transcribed by an automatic speech recognition system, and translated with a baseline statistical machine translation system. We then use information retrieval in a large text corpus in the target language in order to extract parallel sentences/phrases. We evaluate the quality of the extracted data on an English to French translation task and show significant improvements over a state-of-the-art baseline.

Information

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 4: Machine Translation Using Comparable Corpora , July 2016 , pp. 603 - 625
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

†

This work has been partially funded by the French Government under the project DEPART.

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