Specializing Multilingual Language Models: An Empirical Study
Ethan C. Chau, Noah A. Smith · 2021
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.