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1
LISN @ WMT 2021
In: Proceedings of the Sixth Conference on Machine Translation (WMT), ; 6th Conference on Statistical Machine Translation ; https://hal.archives-ouvertes.fr/hal-03430610 ; 6th Conference on Statistical Machine Translation, Association for Computational Linguistics, Nov 2021, Punta Cuna, Dominica ; http://statmt.org/wmt21/program.html (2021)
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2
Using lexical and terminological resources in neural machine translation ; Utilisation de ressources lexicales et terminologiques en traduction neuronale
In: https://hal.archives-ouvertes.fr/hal-02895535 ; [Rapport de recherche] 2020-001, LIMSI-CNRS. 2020, 56 p (2020)
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Using lexical and terminological resources in neural machine translation ; Utilisation de ressources lexicales et terminologiques en traduction neuronale
In: https://hal.archives-ouvertes.fr/hal-02895535 ; [Rapport de recherche] 2020-001, LIMSI-CNRS. 2020, 56 p (2020)
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4
Extrinsic evaluation of sentence alignment systems
In: Abdul-Rauf, Sadaf; Fishel, Mark; Lambert, Patrik; Noubours, Sandra; Sennrich, Rico (2012). Extrinsic evaluation of sentence alignment systems. In: Workshop on Creating Cross-language Resources for Disconnected Languages and Styles, Istanbul, 27 May 2012 - 27 May 2012, 6-10. (2012)
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5
Parallel sentence generation from comparable corpora for improved SMT
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 25 (2011) 4, 341-375
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6
Investigations on Translation Model Adaptation Using Monolingual Data
In: Proceedings of the Sixth Workshop on Statistical Machine Translation ; Sixth Workshop on Statistical Machine Translation ; https://hal.archives-ouvertes.fr/hal-00625481 ; Sixth Workshop on Statistical Machine Translation, Jul 2011, Edinburgh, United Kingdom. pp.284-293 (2011)
Abstract: International audience ; Most of the freely available parallel data to train the translation model of a statistical machine translation system comes from very specific sources (European parliament, United Nations, etc). Therefore, there is increasing interest in methods to perform an adaptation of the translation model. A popular approach is based on unsupervised training, also called self-enhancing. Both only use monolingual data to adapt the translation model. In this paper we extend the previous work and provide new insight in the existing methods. We report results on the translation between French and English. Improvements of up to 0.5 BLEU were observed with respect to a very competitive baseline trained on more than 280M words of human translated parallel data.
Keyword: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; model adaptation; statistical machine translation; translation model
URL: https://hal.archives-ouvertes.fr/hal-00625481
https://hal.archives-ouvertes.fr/hal-00625481/file/11_07_wmt_adaptationWithMonolingualData.pdf
https://hal.archives-ouvertes.fr/hal-00625481/document
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7
SMT and SPE Machine Translation Systems for WMT'09
In: Proceedings of the Fourth Workshop on Statistical Machine Translation ; Fourth Workshop on Statistical Machine Translation ; https://hal.archives-ouvertes.fr/hal-00424686 ; Fourth Workshop on Statistical Machine Translation, Mar 2009, Athens, Greece. pp.130--134 (2009)
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8
On the use of comparable corpora to improve SMT performance
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 12 (2009), 16-23
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9
On the use of Comparable Corpora to improve SMT performance
In: European Chapter of the Association for Computational Linguistics (EACL) ; https://hal.archives-ouvertes.fr/hal-01454950 ; European Chapter of the Association for Computational Linguistics (EACL), 2009, Athens, Greece. pp.16--23 (2009)
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