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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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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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| 4 |
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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| 5 |
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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| 6 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
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| 7 |
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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| 8 |
Sentence Level Embedding Detoxification via Toxic Component Removal ...
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: University of Virginia, 2022
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| 9 |
MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
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In: Applied Sciences; Volume 12; Issue 4; Pages: 2081 (2022)
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| 10 |
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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| 12 |
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
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In: Applied Sciences; Volume 12; Issue 4; Pages: 2185 (2022)
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| 13 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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| 14 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
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Abstract:
Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing.In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. The experimental investigation proves that our approach is able to achieve results (>98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. Our simultaneous diacritics restoration and typos correction approach reaches >94% alpha-word accuracy on the 13 languages. It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches. We also demonstrate all the accuracies to further improve with more training. Taken together, this shows the great real-world application potential of our suggested methods to more data, languages, and error classes.
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Keyword:
ByT5; diacritics restoration; natural language processing; QWERTY; transformer models; typo correction
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URL: https://doi.org/10.3390/app12052636
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| 15 |
Language Competition and Language Shift in Friuli-Venezia Giulia: Projection and Trajectory for the Number of Friulian Speakers to 2050
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In: Sustainability; Volume 14; Issue 6; Pages: 3319 (2022)
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| 16 |
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages
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In: Entropy; Volume 24; Issue 2; Pages: 259 (2022)
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| 17 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
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In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
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| 19 |
Language and vision in conceptual processing: Multilevel analysis and statistical power ...
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| 20 |
Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
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