Learning to Learn Morphological Inflection for Resource-Poor Languages

• URL: http://arxiv.org/abs/2004.13304v1
• Date: Tue, 28 Apr 2020 05:13:17 GMT
• Title: Learning to Learn Morphological Inflection for Resource-Poor Languages
• Authors: Katharina Kann, Samuel R. Bowman, Kyunghyun Cho
• Abstract summary: 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. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines.
• Score: 105.11499402984482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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