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Revisiting Multi-Domain Machine Translation
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03159744 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, 9, pp.17-35. ⟨10.1162/tacl_a_00351⟩ (2021)
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Revisiting Multi-Domain Machine Translation
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03159743 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, 9, pp.17-35 (2021)
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Boosting Neural Machine Translation with Similar Translations
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In: Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02956324 ; Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle, United States. pp.1570-1579, ⟨10.18653/v1/2020.acl-main.143⟩ (2020)
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Generic and Specialized Word Embeddings for Multi-Domain Machine Translation
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02343215 ; International Workshop on Spoken Language Translation, Nov 2019, Hong-Kong, China. ⟨10.5281/zenodo.3524978⟩ ; https://zenodo.org/communities/iwslt2019/ (2019)
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Fixing Translation Divergences in Parallel Corpora for Neural MT
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In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ; Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01908309 ; Conference on Empirical Methods in Natural Language Processing, Nov 2018, Bruxelles, Belgium. pp.2967 - 2973 (2018)
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A multilingual corpus of tweets for domain adaptation
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In: https://hal.archives-ouvertes.fr/hal-01529857 ; [Research Report] LIMSI-CNRS; ELDA; Systran. 2017 (2017)
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N-code: an open-source Bilingual N-gram SMT Toolkit
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In: ISSN: 1804-0462 ; The Prague Bulletin of Mathematical Linguistics ; https://hal.archives-ouvertes.fr/hal-01960706 ; The Prague Bulletin of Mathematical Linguistics, Univerzita Karlova v Praze, 2011, 96, pp.49-58 (2011)
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Discriminative Alignment Training without Annotated Data for Machine Translation
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In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers ; Conference of the North American Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-00430864 ; Conference of the North American Chapter of the Association for Computational Linguistics, Apr 2007, United States. pp.85-88 (2007)
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Abstract:
International audience ; In present Statistical Machine Translation (SMT) systems, alignment is trained in a previous stage as the translation model. Consequently, alignment model parameters are not tuned in function of the translation task, but only indirectly. In this paper, we propose a novel framework for discriminative training of alignment models with automated translation metrics as maximization criterion. In this approach, alignments are optimized for the translation task. In addition, no link labels at the word level are needed. This framework is evaluated in terms of automatic translation evaluation metrics, and an improvement of translation quality is observed.
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Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; discriminative training; machine translation; word alignment
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URL: https://hal.archives-ouvertes.fr/hal-00430864/file/07_04_naacl_discrAlignTraining.pdf
https://hal.archives-ouvertes.fr/hal-00430864/document
https://hal.archives-ouvertes.fr/hal-00430864
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