Hybrid Multilingual Models with Vocabulary Augmentation and Script Transliteration for Low-Resource POS Tagging

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 goal: How does the combination of vocabulary augmentation and script transliteration impact the cross-domain robustness of hybrid multilingual models when evaluated on low-resource language part-of-speech tagging across different text domains (e.g., social media, legal, medical)?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.