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From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
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In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
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Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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Genetic Neural Architecture Search for automatic assessment of human sperm images
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal.archives-ouvertes.fr/hal-03585035 ; Expert Systems with Applications, Elsevier, 2022 (2022)
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Automatic Speech Recognition and Query By Example for Creole Languages Documentation
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In: Findings of the Association for Computational Linguistics: ACL 2022 ; https://hal.archives-ouvertes.fr/hal-03625303 ; Findings of the Association for Computational Linguistics: ACL 2022, May 2022, Dublin, Ireland (2022)
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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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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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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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Abstract:
This paper presents an overview of the state of the art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways : to learn representations for linguistic entities ; to generate coherent utterances and answer questions; to perform utterance transformations, an illustration being their automatic translation. These different facets of the architecture will be successively presented, which will also allow us to discuss its limitations. ; Cet article présente un survol de l’état de l’art en traitement automatique des langues, en explorant une architecture computationnelle, le modèle Transformer, qui joue un rôle central dans une large gamme d’applications. Cette architecture condense de nombreuses avancées des méthodes d’apprentissage neuronales et peut être exploitée de multiples manières : pour apprendre à représenter les entités linguistiques ; pour générer des énoncés cohérents et répondre à des questions ; pour réaliser des transformations des énoncés, une illustration étant leur traduction automatique. Ces différentes facettes de l’architecture seront successivement présentées, ce qui permettra également d’évoquer ses limitations.
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Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Apprentissage Automatique; Language Models; Machine Learning; Modèles de Langues; Natural Language Processing; Neural Machine Translation; Traduction automatique neuronale; Traitement Automatique des Langues
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URL: https://doi.org/10.51257/a-v1-in195 https://hal.archives-ouvertes.fr/hal-03619077/file/Transformers.pdf https://hal.archives-ouvertes.fr/hal-03619077/document https://hal.archives-ouvertes.fr/hal-03619077
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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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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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Neural machine translation and language teaching : possible implications for the CEFR ...
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Estimating Vocal Tract Resonances of Synthesized High-Pitched Vowels Using CNN ...
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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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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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Chess AI: Competing Paradigms for Machine Intelligence
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In: Entropy; Volume 24; Issue 4; Pages: 550 (2022)
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Singing voice separation using waveform-level deep neural networks ... : Διαχωρισμός Φωνητικών χρησιμοποιώντας Βαθιά Νευρωνικά Δίκτυα σε Επίπεδο Κυματομορφών ...
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Καταστολή ηχητικού θορύβου μέσω τεχνικών μηχανικής μάθησης ...
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Can distributional semantics explain performance on the false belief task? ...
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