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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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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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Abstract:
En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip). Their main use should be in-domain translation of social surveys. Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on MCSQ test set (BLEU): en->ru: 64.3 (train: genuine in-domain MCSQ data) ru->en: 74.7 (train: additional backtranslated in-domain MCSQ data) (Evaluated using multeval: https://github.com/jhclark/multeval)
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Keyword:
machine translation; neural machine translation; transformer
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URL: http://hdl.handle.net/11234/1-4681
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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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