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arXiv · 2106.09063

Specializing Multilingual Language Models: An Empirical Study

Ethan C. Chau·Noah A. Smith
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Abstract

Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary augmentation and script transliteration. Our evaluations on part-of-speech tagging, universal dependency parsing, and named entity recognition in nine diverse low-resource languages uphold the viability of these approaches while raising new questions around how to optimally adapt multilingual models to low-resource settings.

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BibTeXRIS

Ethan C. Chau, Noah A. Smith. 2021-06-16. Specializing Multilingual Language Models: An Empirical Study. https://doi.org/10.18653/v1/2021.mrl-1.5

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