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Neural MT and Human Post-editing : a Method to Improve Editorial Quality
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In: ISSN: 1134-8941 ; Interlingüística ; https://halshs.archives-ouvertes.fr/halshs-03603590 ; Interlingüística, Alacant [Spain] : Universitat Autònoma de Barcelona, 2022, pp.15-36 (2022)
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Machine Translation and Gender biases in video game localisation: a corpus-based analysis
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In: https://hal.archives-ouvertes.fr/hal-03540605 ; 2022 (2022)
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Multi-domain Neural Machine Translation ; Traduction automatique neuronale multidomaine
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In: https://tel.archives-ouvertes.fr/tel-03546910 ; Artificial Intelligence [cs.AI]. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG109⟩ (2021)
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A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units
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In: Sensors ; Volume 21 ; Issue 19 (2021)
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Abstract:
Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf–MSA, Nile–MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).
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Keyword:
Arabic dialects; modern standard Arabic; multi-head attention; neural machine translation (NMT); self-attention; shared vocabulary; subword units; transformer
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URL: https://doi.org/10.3390/s21196509
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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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Cadlaws - An Enlgish-French parallel corpus of legally equivalent documents
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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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Machine Translation for the Normalisation of 17th c. French ; Traduction automatique pour la normalisation du français du XVII e siècle
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In: TALN 2020 ; https://hal.archives-ouvertes.fr/hal-02596669 ; TALN 2020, ATALA, Jun 2020, Nancy, France (2020)
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Bridging the “gApp”: improving neural machine translation systems for multiword expression detection
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In: 11 ; 1 ; 61 ; 80 (2020)
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| 11 |
Neural MT and Human Post-editing: a Method to Improve Editorial Quality
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In: Symposium Translation and Knowledge Transfer: News trends in the theory and practice of translation and interpreting ; https://hal.univ-rennes2.fr/hal-02495919 ; Symposium Translation and Knowledge Transfer: News trends in the theory and practice of translation and interpreting, Mar Ogea-Pozo, Carmen Expósito-Castro, Oct 2019, Cordoue, Spain (2019)
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Measuring the Impact of Neural Machine Translation on Easy-to-Read Texts: An Exploratory Study
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In: Conference on Easy-to-Read Language Research (Klaara 2019) (2019) (2019)
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Preferences of end-users for raw and post-edited NMT in a business environment
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In: ISBN: 978-2970-10957-0 ; Proceedings of the 41st Conference Translating and the Computer pp. 47-59 (2019)
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