| 1 |
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
|
|
|
|
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
|
|
|
|
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)
|
|
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.
|
|
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
|
|
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
|
|
BASE
|
|
Hide details
|
|
|
| 4 |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages ...
|
|
Chen, Fuxiang. - : Federated Research Data Repository / dépôt fédéré de données de recherche, 2022
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Sentence Level Embedding Detoxification via Toxic Component Removal ...
|
|
: University of Virginia, 2022
|
|
BASE
|
|
Show details
|
|
|
| 9 |
MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2081 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
|
|
|
|
In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2185 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
|
|
|
|
In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
|
|
|
|
In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Language Competition and Language Shift in Friuli-Venezia Giulia: Projection and Trajectory for the Number of Friulian Speakers to 2050
|
|
|
|
In: Sustainability; Volume 14; Issue 6; Pages: 3319 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages
|
|
|
|
In: Entropy; Volume 24; Issue 2; Pages: 259 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
|
|
|
|
In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Language and vision in conceptual processing: Multilevel analysis and statistical power ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
