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Applying Morphological Decompositions to Statistical Machine Translation.

, , , and . WMT@ACL, page 195-200. Association for Computational Linguistics, (2010)

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First-Pass Techniques for Very Large Vocabulary Speech Recognition ff Morphologically Rich Languages., , and . SLT, page 227-234. IEEE, (2018)Language Identification of Short Text Segments with N-gram Models., , and . LREC, European Language Resources Association, (2010)Applying Morphological Decompositions to Statistical Machine Translation., , , and . WMT@ACL, page 195-200. Association for Computational Linguistics, (2010)Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian., , and . Comput. Speech Lang., (2021)Morpho Challenge 2005-2010: Evaluations and Results., , , and . SIGMORPHON, page 87-95. Association for Computational Linguistics, (2010)Predicting Reaction Times in Word Recognition by Unsupervised Learning of Morphology., , , , and . ICANN (1), volume 6791 of Lecture Notes in Computer Science, page 275-282. Springer, (2011)Morfessor and Hutmegs: unsupervised morpheme segmentation for highly-inflecting and compounding languages, , , and . (2005)Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation., , and . Mach. Transl., 34 (4): 251-286 (2020)Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon., , , , , and . Cogn. Sci., 42 (3): 939-973 (2018)Democratizing Machine Translation with OPUS-MT., , , , , , , , , and . CoRR, (2022)