2020
ACL
ACL 2020
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Interdisciplinary
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Hot Topic Early Bird
- low-resource language
Authors
Topics
Machine Learning > Learning Types > Active Learning
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Application Areas > Data Augmentation
Interdisciplinary > Linguistics > Morphology
Machine Learning > Learning Types > Few-Shot Learning
Deep Learning > Learning Types > Ensemble Learning
Deep Learning > Learning Types > Data Augmentation