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Opening the Vocabulary of Neural Language Models with Character-level Word Represen- Tations

, and . (2016)

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Training Continuous Space Language Models: Some Practical Issues., , , and . EMNLP, page 778-788. ACL, (2010)LIMSI-MULTISEM at the IJCAI SemDeep-5 WiC Challenge: Context Representations for Word Usage Similarity Estimation., , and . SemDeep@IJCAI, page 6-11. Association for Computational Linguistics, (2019)Learning dynamic word embeddings with drift regularisation., and . CoRR, (2019)Transcribing audio-video archives., , , and . ICASSP, page 13-16. IEEE, (2002)Diachronic vocabulary adaptation for broadcast news transcription., and . INTERSPEECH, page 1305-1308. ISCA, (2005)Character and Subword-Based Word Representation for Neural Language Modeling Prediction., and . SWCN@EMNLP, page 1-13. Association for Computational Linguistics, (2017)Discriminant Models for Word Alignment., and . Trait. Autom. des Langues, 50 (3): 173-203 (2009)Algorithmes à base d'échantillonage pour l'entraînement de modèles de langue neuronaux (Here the title in English)., and . CORIA-TALN-RJC (TALN 2), page 455-464. ATALA, (2018)FlauBERT : des modèles de langue contextualisés pré-entraînés pour le français (FlauBERT : Unsupervised Language Model Pre-training for French)., , , , , , , , , and . JEP-TALN-RECITAL (2), page 268-278. ATALA et AFCP, (2020)LIMSI @ IWSLT 2010., , , , , and . IWSLT, page 105-112. ISCA, (2010)