| 1 |
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)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Representation learning of natural language and its application to language understanding and generation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Neural-based Knowledge Transfer in Natural Language Processing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Impact of textual data augmentation on linguistic pattern extraction to improve the idiomaticity of extractive summaries
|
|
|
|
In: Lecture Notes in Computer Science ; https://hal.archives-ouvertes.fr/hal-03271380 ; Matteo Golfarelli; Robert Wrembel. Lecture Notes in Computer Science, Springer, In press, Lecture Notes in Computer Science (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
The Mapping of Deep Language Models on Brain Responses Primarily Depends on their Performance
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03361439 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
A logistic regression model for predicting child language performance ; Un modèle de régression logistique pour la prédiction du développement langagier chez l'enfant
|
|
|
|
In: SIS 2021, 50th Annuale Conference of the Italian Statistical Society" ; https://hal.archives-ouvertes.fr/hal-03318721 ; SIS 2021, 50th Annuale Conference of the Italian Statistical Society", Jun 2021, Pise, Italy (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
|
|
|
|
In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
|
|
Abstract:
International audience ; In this paper, we propose a novel architecture called Hierarchical-Task Reservoir (HTR) suitable for real-time applications for which different levels of abstraction are available. We apply it to semantic role labeling based on continuous speech recognition. Taking inspiration from the brain, that demonstrates hierarchies of representations from perceptive to integrative areas, we consider a hierarchy of four sub-tasks with increasing levels of abstraction (phone, word, part-of-speech and semantic role tags). These tasks are progressively learned by the layers of the HTR architecture. Interestingly, quantitative and qualitative results show that the hierarchical-task approach provides an advantage to improve the prediction. In particular, the qualitative results show that a shallow or a hierarchical reservoir, considered as baselines, do not produce estimations as good asthe HTR model would. Moreover, we show that it is possible to further improve the accuracy of the model by designing skip connections and by considering word embedding in the internal representations. Overall, the HTR outperformed the other stateof-the-art reservoir-based approaches and it resulted in extremely efficient w.r.t. typical RNNs in deep learning (e.g. LSTMs). The HTR architecture is proposed as a step toward the modeling of online and hierarchical processes at work in the brain during language comprehension.
|
|
Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.LING]Cognitive science/Linguistics; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; Anytime Process; Hierarchical Processing; Hierarchical Reservoir Computing; Natural Language Processing; Part-of-Speech; POS tagging; Recurrent Neural Networks; Semantic Role Labelling; Speech Recognition
|
|
URL: https://hal.inria.fr/hal-03031413
https://doi.org/10.1109/TNNLS.2021.3095140
https://hal.inria.fr/hal-03031413v3/file/PedrelliHinaut2020_preprint_HAL-v3.pdf
https://hal.inria.fr/hal-03031413v3/document
|
|
BASE
|
|
Hide details
|
|
|
| 10 |
On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Multilingual Email Zoning - Segmenting Multilingual Email Text Into Zones
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
