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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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| 2 |
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
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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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The use of MT by undergraduate translation students for different learning tasks
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In: https://hal.archives-ouvertes.fr/hal-03547415 ; 2022 (2022)
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| 4 |
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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| 5 |
Neural machine translation and language teaching : possible implications for the CEFR ...
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| 6 |
MCSQ Translation Models (en-ru) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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| 7 |
MCSQ Translation Models (en-de) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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| 8 |
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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| 10 |
An Initial Investigation of Neural Decompilation for WebAssembly ; En Första Undersökning av Neural Dekompilering för WebAssembly
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Benali, Adam. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2022
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| 11 |
Lexical Diversity in Statistical and Neural Machine Translation
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In: Information; Volume 13; Issue 2; Pages: 93 (2022)
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Abstract:
Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss in lexical variation, evidence of “machine translationese”, and its effect on post-editing, which results in “post-editese”. In this study, we analyze the outputs of three English to Slovenian machine translation systems in terms of lexical diversity in three different genres. Using both quantitative and qualitative methods, we analyze one statistical and two neural systems, and we compare them to a human reference translation. Our quantitative analyses based on lexical diversity metrics show diverging results; however, translation systems, particularly neural ones, mostly exhibit larger lexical diversity than their human counterparts. Nevertheless, a qualitative method shows that these quantitative results are not always a reliable tool to assess true lexical diversity and that a lot of lexical “creativity”, especially by neural translation systems, is often unreliable, inconsistent, and misguided.
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Keyword:
lexical diversity; machine translation; measure of textual lexical diversity; neural translation systems; type-token ratio
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URL: https://doi.org/10.3390/info13020093
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| 12 |
A Survey of Automatic Source Code Summarization
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In: Symmetry; Volume 14; Issue 3; Pages: 471 (2022)
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| 13 |
Neural Models for Measuring Confidence on Interactive Machine Translation Systems
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1100 (2022)
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| 14 |
Impact of Sentence Representation Matching in Neural Machine Translation
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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| 15 |
Retrieval-Based Transformer Pseudocode Generation
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In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022)
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| 16 |
Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation
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In: Information; Volume 13; Issue 1; Pages: 19 (2022)
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| 17 |
Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents
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| 18 |
Neural-based Knowledge Transfer in Natural Language Processing
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| 19 |
Investigating alignment interpretability for low-resource NMT
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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| 20 |
Gender Bias in Neural Translation: a preliminary study ; Biais de genre dans un système de traduction automatique neuronale : une étude préliminaire
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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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