Typological Distance and Accuracy Degradation in Zero-Shot Cross-Lingual Transfer

Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot cross-lingual transfer of POS and NER tagging to Bodo. We explore two distinct methodologies: (1) direct translation of English sentences to Bodo followed by tag transfer, and (2) prompt-based tag transfer on parallel English-Bodo sentence p

Research goal: How does the typological distance between English and target languages correlate with accuracy degradation in zero-shot cross-lingual transfer after intermediate-task training on XTREME NER and POS tagging tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.