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1
European Language Equality - Report on the French Language
In: https://hal.archives-ouvertes.fr/hal-03637776 ; [Research Report] CNRS - LISN. 2022 (2022)
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2
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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3
Graph Neural Networks for Multiparallel Word Alignment ...
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4
LIMSI @ WMT’14 Medical Translation Task
Yvon, François; Do, Quoc Khanh; Lavergne, Thomas. - : Association for Computational Linguistics, 2022
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5
Optimizing Word Alignments with Better Subword Tokenization
In: The 18th biennial conference of the International Association of Machine Translation ; https://hal.archives-ouvertes.fr/hal-03322842 ; The 18th biennial conference of the International Association of Machine Translation, Aug 2021, Miami (virtual), United States (2021)
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6
Gender Bias in Neural Translation: a preliminary study ; Biais de genre dans un système de traduction automatique neuronale : une étude préliminaire
In: Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale ; Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03265895 ; Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.11-25 ; https://talnrecital2021.inria.fr/ (2021)
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7
Screening Gender Transfer in Neural Machine Translation
In: Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, ; Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP ; https://hal.archives-ouvertes.fr/hal-03424174 ; Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, Association for computational linguistics, Nov 2021, Punta Cana, Dominica ; https://blackboxnlp.github.io/ (2021)
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8
Graph Algorithms for Multiparallel Word Alignment
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; The 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03424044 ; The 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Nov 2021, Punta Cana, Dominica ; https://2021.emnlp.org/ (2021)
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9
Can You Traducir This? Machine Translation for Code-Switched Input
In: Workshop on Computational Approaches to Linguistic Code Switching ; https://hal.archives-ouvertes.fr/hal-03218889 ; Workshop on Computational Approaches to Linguistic Code Switching, Association for Computational Linguistics, Jun 2021, Online, United States (2021)
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10
One Source, Two Targets: Challenges and Rewards of Dual Decoding
In: Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03345478 ; Conference on Empirical Methods in Natural Language Processing, Nov 2021, Online and Punta Cana, Dominican Republic ; https://2021.emnlp.org/ (2021)
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11
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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12
Segmentation en mots faiblement supervisée pour la documentation automatique des langues
In: https://hal.archives-ouvertes.fr/hal-03477475 ; 2021 (2021)
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13
Revisiting Multi-Domain Machine Translation
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)
Abstract: International audience ; When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work, that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; domain adaptation; Machine Translation
URL: https://hal.archives-ouvertes.fr/hal-03159744
https://hal.archives-ouvertes.fr/hal-03159744/file/tacl_a_00351.pdf
https://doi.org/10.1162/tacl_a_00351
https://hal.archives-ouvertes.fr/hal-03159744/document
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14
Revisiting Multi-Domain Machine Translation
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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15
Can You Traducir This? Machine Translation for Code-Switched Input ...
Xu, Jitao; Yvon, François. - : arXiv, 2021
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16
Graph Algorithms for Multiparallel Word Alignment ...
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17
One Source, Two Targets: Challenges and Rewards of Dual Decoding ...
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18
Graph Algorithms for Multiparallel Word Alignment ...
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19
Generative latent neural models for automatic word alignment
In: Association for Machine Translation in the Americas ; https://hal.archives-ouvertes.fr/hal-02949042 ; Association for Machine Translation in the Americas, Oct 2020, Miami, Florida, United States. pp.64-77 ; https://amtaweb.org/amta-2020-conference-program-and-registration-are-available/ (2020)
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20
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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