Multilingual Language Models in Multimodal Teacher-Student Learning for Cross-Lingual NER in Low-Resource Settings

Abstract

Abstract: Abstract: Despite the emergence of large-scale multilingual pre-trained models like mBERT, XLM-RoBERTa, and mT5, natural language processing (NLP) still struggles in low-resource languages due to limited annotated data. This paper explores the use of transfer learning to adapt pre-trained multilingual models to low-resource tasks such as Named Entity Recognition (NER), sentiment analysis, and machine translation for languages like Amharic, Hausa, and Sinhala. By leveraging zero-shot and few-shot learning paradigms and evaluating cross-lingual embeddings and token overlap, we demonstrate signif

Research Question

Can the incorporation of pre-trained multilingual language models (e.g., mBERT, XLM-R) further enhance the effectiveness of multimodal teacher-student learning for cross-lingual NER, as evaluated using standard NER benchmarks in low-resource settings?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance