Performance comparison of multi-source and single-source cross-lingual NER models in low-resource languages with noisy target data

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 goal: How does the performance of multi-source cross-lingual NER models compare to single-source models when evaluated on a benchmark like XTREME-NER, particularly in low-resource languages with varying noise levels in the target language data?

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