Vocabulary Augmentation for Robust Cross-Lingual Universal Dependency Parsing Under Domain Shift in Low-Resource Settings

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: Does vocabulary augmentation improve cross-lingual transfer robustness for Universal Dependency Parsing under domain shift conditions in low-resource settings?

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