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dc.contributor.authorBlain, Frederic
dc.contributor.authorSchwenk, Holger
dc.contributor.authorSenellart, Jean
dc.date.accessioned2020-09-03T13:54:50Z
dc.date.available2020-09-03T13:54:50Z
dc.date.issued2012-12-06
dc.identifier.citationBlain, F., Schwenk, H. and Senellart, J. (2012) Incremental adaptation using translation informations and post-editing analysis. In Sumita, E. et al. (eds.) Proceedings of the International Workshop on Spoken Language Translation, 6th-7th December, 2012, Hong Kong.en
dc.identifier.urihttp://hdl.handle.net/2436/623581
dc.descriptionThis is an accepted manuscript of an article published by IWSLT in Proceedings of the International Workshop on Spoken Language Translation 2012, available online: http://hltc.cs.ust.hk/iwslt/proceedings/Proceedings_Iwslt2012.pdf The accepted version of the publication may differ from the final published version.en
dc.description.abstractIt is well known that statistical machine translation systems perform best when they are adapted to the task. In this paper we propose new methods to quickly perform incremental adaptation without the need to obtain word-by-word alignments from GIZA or similar tools. The main idea is to use an automatic translation as pivot to infer alignments between the source sentence and the reference translation, or user correction. We compared our approach to the standard method to perform incremental re-training. We achieve similar results in the BLEU score using less computational resources. Fast retraining is particularly interesting when we want to almost instantly integrate user feed-back, for instance in a post-editing context or machine translation assisted CAT tool. We also explore several methods to combine the translation models.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherIWSLTen
dc.relation.urlhttp://hltc.cs.ust.hk/iwslt/proceedings/Proceedings_Iwslt2012.pdfen
dc.titleIncremental adaptation using translation informations and post-editing analysisen
dc.typeConference contributionen
dc.date.updated2020-08-25T14:38:36Z
rioxxterms.funderUniversite du Maine, Le Mansen
rioxxterms.identifier.projectUOW03092020FBen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-09-03en
dc.source.beginpage234
dc.source.endpage241
refterms.dateFCD2020-09-03T11:54:07Z
refterms.versionFCDAM


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