Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection

By Xiang Yu, Ngoc Thang Vu, Jonas Kuhn. 2020

15 citations recorded by the source corpus.

We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style. We apply this framework on two SIGMORPHON 2020 shared tasks: grapheme-to-phoneme conversion and morphological inflection. With very simple base models in the ensemble, we rank the first and the fourth in these two tasks. We show in the analysis that our system works especially well on low-resource languages.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models (2021, neutral): “On grapheme-to-phoneme, ByT5 beats the state-of-art model of Yu et al. (2020) (PER: 1.6 vs. 2.8).”
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection (2020, background): “Uralic Finnic est Estonian UniMorph Finnic fin Finnish UniMorph Finnic izh Ingrian UniMorph Finnic krl Karelian Zaytseva et al. (2017) Finnic liv Livonian UniMorph Finnic vep Veps Zaytseva et al.”
The SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion (2020, methodology): “IMS A single submission from the Institut für Maschinelle Sprachverarbeitung at the University of Stuttgart (Yu et al. 2020) uses self-training (Yarowsky 1995) and ensembles of the baseline models.”