Source Corpus Size Variability and Cross-Lingual NER Transfer Accuracy on Noisy Low-Resource Test Sets

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

To what extent does varying the size of the source language corpus impact the cross-lingual transfer accuracy of projection-based NER models on noisy low-resource test sets?

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