University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Titre : Innovative Deep Neural Network Models for Multilingual Translation Type de document : texte imprimé Auteurs : rokia Bencheikh, Auteur ; roumaissa Boulgamer, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse Année de publication : 2022 Importance : 1 vol (87 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine Translation
Multilingual translationIndex. décimale : 004 Informatique Résumé :
Machine Translation is one of the most prominent-difficult topic in Natural Language Processing,
based on building machine learning models which can understand human languages and predict
a target translation from a source expression.
Recently, There have been a big step to bring efficient language models that can understand
and process the huge amount of generated textual data. The Attention mechanism and the
Transformer model play a role model, where recent studies revealed that they achieved higher
performance in all NLP tasks.
This work presents a detailed comparative study between statistical approaches and advances
ones applied to Natural language processing, mainly in Neural machine Translation task.
In this work, 10 models in total were replicated trained then validated on 2 datasets for monolingual
translation , and 2 for multilingual translation: ( Turkish-English and Spanish-English)
and for many translation direction (Turkish-English, English-Turkish, Spanish-English, English-
Spanish, Turkish-Spanish). The experiments show that Transformer based models tend to give
better performance despite of linguistic complexities for natural languages such Turkish, Spanish
and English in both translation tasks: monolingual-translation and multilingual-translation.Côte titre : MAI/0613 En ligne : https://drive.google.com/file/d/10Iq4fPFQ6WUh8rWyniUBE0P6-FTi-rwq/view?usp=share [...] Format de la ressource électronique : Innovative Deep Neural Network Models for Multilingual Translation [texte imprimé] / rokia Bencheikh, Auteur ; roumaissa Boulgamer, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse . - 2022 . - 1 vol (87 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine Translation
Multilingual translationIndex. décimale : 004 Informatique Résumé :
Machine Translation is one of the most prominent-difficult topic in Natural Language Processing,
based on building machine learning models which can understand human languages and predict
a target translation from a source expression.
Recently, There have been a big step to bring efficient language models that can understand
and process the huge amount of generated textual data. The Attention mechanism and the
Transformer model play a role model, where recent studies revealed that they achieved higher
performance in all NLP tasks.
This work presents a detailed comparative study between statistical approaches and advances
ones applied to Natural language processing, mainly in Neural machine Translation task.
In this work, 10 models in total were replicated trained then validated on 2 datasets for monolingual
translation , and 2 for multilingual translation: ( Turkish-English and Spanish-English)
and for many translation direction (Turkish-English, English-Turkish, Spanish-English, English-
Spanish, Turkish-Spanish). The experiments show that Transformer based models tend to give
better performance despite of linguistic complexities for natural languages such Turkish, Spanish
and English in both translation tasks: monolingual-translation and multilingual-translation.Côte titre : MAI/0613 En ligne : https://drive.google.com/file/d/10Iq4fPFQ6WUh8rWyniUBE0P6-FTi-rwq/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0613 MAI/0613 Mémoire Bibliothéque des sciences Anglais Disponible
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