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.
InProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (V ol- ume 1: Long Papers), pages 2004–2015
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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.