Autoencoding is the best auxiliary objective for morphological inflection when unlabeled data is tiny; character-level masked language modeling wins with more unlabeled data, and segment oracle masking helps further.
In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 176–203, Seattle, Washing- ton
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Improving Low-Resource Morphological Inflection via Self-Supervised Objectives
Autoencoding is the best auxiliary objective for morphological inflection when unlabeled data is tiny; character-level masked language modeling wins with more unlabeled data, and segment oracle masking helps further.