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A Reception Study of Machine-Translated Easy Language Text by Individuals with Reading Difficulties
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In: 3rd International Conference on Translation, Interpreting and Cognition (ICTIC3) (2021) (2021)
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Suitability of Neural Machine Translation for Producing Linguistically Accessible Text: Exploring the Effects of Pre-Editing on Easy-to-Read Administrative Documents
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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Transformer-based NMT : modeling, training and implementation
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Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021
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Comparing Encoder-Decoder Architectures for Neural Machine Translation: A Challenge Set Approach
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Cadlaws - An Enlgish-French parallel corpus of legally equivalent documents
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A study on the impact of neural architectures for Unsupervised Machine Translation
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On The Use of Monolingual Corpus for Training Neural Machine Translation Systems ; Uso de corpus monolíngües para el entrenamiento de traductores neuronales
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Cadlaws – An English–French Parallel Corpus of Legally Equivalent Documents
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In: Mutatis Mutandis: Revista Latinoamericana de Traducción, ISSN 2011-799X, Vol. 14, Nº. 2, 2021 (Ejemplar dedicado a: Nuevas perspectivas de investigación en la traducción especializada en lenguas románicas: aspectos comparativos, léxicos, fraseológicos, discursivos y didácticos), pags. 494-508 (2021)
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Limitations and challenges of unsupervised cross-lingual pre-training
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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
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In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 2-6 Nov 2020, Lisboa, Portugal. (2020)
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Rapid development of competitive translation engines for access to multilingual COVID-19 information
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In: Way, Andy orcid:0000-0001-5736-5930 , Haque, Rejwanul orcid:0000-0003-1680-0099 , Xie, Guodong, Gaspari, Federico orcid:0000-0003-3808-8418 , Popović, Maja orcid:0000-0001-8234-8745 and Poncelas, Alberto orcid:0000-0002-5089-1687 (2020) Rapid development of competitive translation engines for access to multilingual COVID-19 information. Informatics . ISSN 2227-9709 (2020)
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Investigating query expansion and coreference resolution in question answering on BERT
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In: Bhattacharjee, Santanu, Haque, Rejwanul orcid:0000-0003-1680-0099 , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2020) Investigating query expansion and coreference resolution in question answering on BERT. In: 25th International Conference on Natural Language & Information Systems (NLDB 2020)), 24 - 26 June 2020, Saarbrücken, Germany (Online). ISBN 978-3-030-51309-2 (2020)
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A human evaluation of English-Irish statistical and neural machine translation
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Castilho, Sheila orcid:0000-0002-8416-6555 , Moorkens, Joss orcid:0000-0003-4864-5986 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2020) A human evaluation of English-Irish statistical and neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 6 Nov 2020, Lisbon, Portugal. (2020)
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Approches quantitatives de l'analyse des prédictions en traduction automatique neuronale (TAN)
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In: JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles ; https://hal.archives-ouvertes.fr/hal-03049589 ; JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles, Université de Toulouse, Jun 2020, Toulouse, France ; https://jadt2020.sciencesconf.org (2020)
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Using lexical and terminological resources in neural machine translation ; Utilisation de ressources lexicales et terminologiques en traduction neuronale
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In: https://hal.archives-ouvertes.fr/hal-02895535 ; [Rapport de recherche] 2020-001, LIMSI-CNRS. 2020, 56 p (2020)
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Abstract:
Neural machine translation (MT) has led to a clear improvement in translation quality, increasing the usability of the automatically translated texts in a variety of contexts. This technology is based on the use of black-box algorithms, which makes it difficult to control the translation process. In particular, while the previous generation of (statistical) translation models were easily completed with dictionary or terminological resources, the hybridization of neural MT with dictionary or rule-based methods is much more challenging. This is sometimes experienced as a step backwards, especially in computer-assisted translation (CAT) or post-editing (PE) contexts, or in contexts or domains for which parallel data is scarce. In this report, we develop a critical review of recent attempts to integrate bilingual lexicons into neural MT, and find that most of them can be interpreted as ways to adapt older methods to the neural MT framework. We also discuss various avenues that remain to be explored to make this hybridization of MT more predictable and transparent. ; La traduction automatique (TA) neuronale a conduit à une amélioration perceptible de la qualité de traduction et de l'utilisabilité des textes ainsi produits dans un nombre varié de contextes. Cette technologie repose sur l'exploitation d'algorithmes qui fonctionnent en boite noire, ce qui rend difficile le contrôle fin du processus de traduction. En particulier, alors que la génération antérieure de modèles de traduction (statistique) permettait assez directement d'injecter des ressources dictionnairiques ou terminologiques, l'hybridation de la TA neuronale par des méthodes à base de dictionnaires ou de règles s'avère plus délicate. Ceci est parfois vécu comme une régression, en particulier dans des contextes de traduction assistée par ordinateur (TAO) ou de post-édition (PE), ou encore dans les contextes ou domaines pour lesquels il existe peu de données parallèles. Dans ce rapport, nous proposons une revue critique des tentatives récentes pour intégrer des lexiques bilingues en TA neuronales, pour constater que la plupart peuvent s'interpréter comme des essais pour adapter au cadre de la TA neuronale des méthodes anciennes. Nous discutons également diverses pistes qui restent à explorer pour rendre cette hybridation de la TA plus prédictible et plus transparente.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Neural machine translation; Ressources terminologiques; Terminological resources; Traduction automatique neuronale
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URL: https://hal.archives-ouvertes.fr/hal-02895535 https://hal.archives-ouvertes.fr/hal-02895535v4/document https://hal.archives-ouvertes.fr/hal-02895535v4/file/d2-1.pdf
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Using lexical and terminological resources in neural machine translation ; Utilisation de ressources lexicales et terminologiques en traduction neuronale
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In: https://hal.archives-ouvertes.fr/hal-02895535 ; [Rapport de recherche] 2020-001, LIMSI-CNRS. 2020, 56 p (2020)
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Joint Neural Models of Word Sense Disambiguation and Machine Translation ; Modèles neuronaux joints de désambiguïsation lexicale et de traduction automatique
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In: https://tel.archives-ouvertes.fr/tel-03027723 ; Informatique et langage [cs.CL]. Université Grenoble Alpes, 2020. Français (2020)
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Joint Neural Models of Word Sense Disambiguation and Machine Translation ; Modèles neuronaux joints de désambiguïsation lexicale et de traduction automatique
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In: https://tel.archives-ouvertes.fr/tel-03033118 ; Intelligence artificielle [cs.AI]. Université Grenoble Alpes [2020-.], 2020. Français. ⟨NNT : 2020GRALM032⟩ (2020)
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