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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
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In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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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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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2022 (2022)
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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.
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In: Nature communications, vol 13, iss 1 (2022)
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Changes in the midst of a construction network: a diachronic construction grammar approach to complex prepositions denoting internal location
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In: ISSN: 0936-5907 ; EISSN: 1613-3641 ; Cognitive Linguistics ; https://halshs.archives-ouvertes.fr/halshs-03637056 ; Cognitive Linguistics, De Gruyter, 2022, ⟨10.1515/cog-2021-0128⟩ (2022)
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Changes in the midst of a construction network: a diachronic construction grammar approach to complex prepositions denoting internal location
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In: ISSN: 0936-5907 ; EISSN: 1613-3641 ; Cognitive Linguistics ; https://halshs.archives-ouvertes.fr/halshs-03637056 ; Cognitive Linguistics, De Gruyter, In press, ⟨10.1515/cog-2021-0128⟩ (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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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
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From bag-of-words towards natural language: adapting topic models to avoid stop word removal ...
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A Collection of Classroom Instruction ... : A Collection of Classroom Instruction ...
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Biodiversity: how big is our global biodiversity debt and what can we do about it? ...
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Bayesian data analysis in the phonetic sciences: A tutorial introduction ...
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How Cognitive Abilities May Support Children’s Bilingual Literacy Development in a Multilingual Society ...
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On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages ...
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Chen, Fuxiang. - : Federated Research Data Repository / dépôt fédéré de données de recherche, 2022
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