Domain Adaptation in Multi-Source Cross-Lingual NER for Robustness Against Character-Level Noise

Abstract

Abstract: Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we pres

Research Question

How does domain adaptation in multi-source cross-lingual NER models improve robustness against character-level noise in low-resource languages, as evaluated using the MLQA benchmark?

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This record has passed Gate 2: a Lean4 proof source type-checks, or a sealed-sandbox run reproduced the reported results within the stated tolerance. A reproducible artifact (proof source or repro script and results) is attached to this record. 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.

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