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Token-level and sequence-level loss smoothing for RNN language models., , и . ACL (1), стр. 2094-2103. Association for Computational Linguistics, (2018)Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity., , , , и . EMNLP (Findings), стр. 12858-12870. Association for Computational Linguistics, (2023)Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English., , , , , и . COLING, стр. 5047-5058. International Committee on Computational Linguistics, (2020)Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages., , , и . EACL, стр. 1505-1519. Association for Computational Linguistics, (2023)Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction., , и . CoNLL, стр. 97-107. Association for Computational Linguistics, (2018)Causes and Cures for Interference in Multilingual Translation., , , , и . ACL (1), стр. 15849-15863. Association for Computational Linguistics, (2023)Added Toxicity Mitigation at Inference Time for Multimodal and Massively Multilingual Translation., , , и . CoRR, (2023)Joint source-target encoding with pervasive attention., , и . Mach. Transl., 35 (4): 637-659 (2021)Depth-Adaptive Transformer., , , и . ICLR, OpenReview.net, (2020)Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation. (Une alternative aux modèles neuronaux séquence-à-séquence pour la traduction automatique).. Grenoble Alpes University, France, (2020)