Cross-lingual STS Method vs Multilingual Language Models in Zero-shot Low-resource Accuracy

Abstract

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.

Research Question

How does the resource-light cross-lingual STS method compare to large multilingual language models in terms of zero-shot accuracy on low-resource language pairs?

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