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Architectures neuronales multilingues pour le traitement automatique des langues naturelles

Abstract : The translation of languages has become an essential need for communication between humans in a world where the possibilities of communication are expanding. Machine translation is a response to this evolving need. More recently, neural machine translation has come to the fore with the great performance of neural systems, opening up a new area of machine learning. Neural systems use large amounts of data to learn how to perform a task automatically. In the context of machine translation, the sometimes large amounts of data needed to learn efficient systems are not always available for all languages.The use of multilingual systems is one solution to this problem. Multilingual machine translation systems make it possible to translate several languages within the same system. They allow languages with little data to be learned alongside languages with more data, thus improving the performance of the translation system. This thesis focuses on multilingual machine translation approaches to improve performance for languages with limited data. I have worked on several multilingual translation approaches based on different transfer techniques between languages. The different approaches proposed, as well as additional analyses, have revealed the impact of the relevant criteria for transfer. They also show the importance, sometimes neglected, of the balance of languages within multilingual approaches.
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Submitted on : Thursday, April 15, 2021 - 4:31:22 PM
Last modification on : Wednesday, January 26, 2022 - 5:35:06 PM
Long-term archiving on: : Friday, July 16, 2021 - 7:02:58 PM


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  • HAL Id : tel-03199494, version 1


Adrien Bardet. Architectures neuronales multilingues pour le traitement automatique des langues naturelles. Informatique et langage [cs.CL]. Le Mans Université, 2021. Français. ⟨NNT : 2021LEMA1002⟩. ⟨tel-03199494⟩



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