Script Transliteration Effects on Multilingual NER Robustness in Low-Resource Non-Latin Languages

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: What is the effect of script transliteration on the robustness of multilingual language models for named entity recognition in non-Latin script low-resource languages?

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