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The WMT'18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English.

, , , , , , , and . WMT (shared task), page 546-560. Association for Computational Linguistics, (2018)

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Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation., , and . Mach. Transl., 34 (4): 251-286 (2020)Democratizing Machine Translation with OPUS-MT., , , , , , , , , and . CoRR, (2022)Hybrid Morphological Segmentation for Phrase-Based Machine Translation., , and . WMT, page 289-295. The Association for Computer Linguistics, (2016)Extending hybrid word-character neural machine translation with multi-task learning of morphological analysis., , and . WMT, page 296-302. Association for Computational Linguistics, (2017)Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning., , and . LREC, page 3944-3953. European Language Resources Association, (2020)Finnish Language Modeling with Deep Transformer Models., , , and . CoRR, (2020)Isotropy, Clusters, and Classifiers., , and . CoRR, (2024)Multimodal machine translation through visuals and speech., , , , , , and . Mach. Transl., 34 (2-3): 97-147 (2020)Multimodal Machine Translation through Visuals and Speech., , , , , , and . CoRR, (2019)LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages., and . WMT@EMNLP, page 411-416. The Association for Computer Linguistics, (2015)