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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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| 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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Abstract:
Reducing the human effort performed with the use of interactive-predictive neural machine translation (IPNMT) systems is one of the main goals in this sub-field of machine translation (MT). Prior works have focused on changing the human–machine interaction method and simplifying the feedback performed. Applying confidence measures (CM) to an IPNMT system helps decrease the number of words that the user has to check through the translation session, reducing the human effort needed, although this supposes losing a few points in the quality of the translations. The effort reduction comes from decreasing the number of words that the translator has to review—it only has to check the ones with a score lower than the threshold set. In this paper, we studied the performance of four confidence measures based on the most used metrics on MT. We trained four recurrent neural network (RNN) models to approximate the scores from the metrics: Bleu, Meteor, Chr-f, and TER. In the experiments, we simulated the user interaction with the system to obtain and compare the quality of the translations generated with the effort reduction. We also compare the performance of the four models between them to see which of them obtains the best results. The results achieved showed a reduction of 48% with a Bleu score of 70 points—a significant effort reduction to translations almost perfect.
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Keyword:
confidence measures; interactive machine translation; machine translation; neural model; quality estimation
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URL: https://doi.org/10.3390/app12031100
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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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