We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters that can serve as a strong initialization point for fine-tuning on a resource-poor target language. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines. In particular, it obtains a 31.7% higher absolute accuracy than a previously proposed cross-lingual transfer model and outperforms the previous state of the art by 1.7% absolute accuracy on average over languages.
@article{arxiv.2004.13304,
title = {Learning to Learn Morphological Inflection for Resource-Poor Languages},
author = {Katharina Kann and Samuel R. Bowman and Kyunghyun Cho},
journal= {arXiv preprint arXiv:2004.13304},
year = {2020}
}