Mitigating Annotation Projection Noise in Cross-Lingual NER via Source Dataset Scaling for Low-Resource Languages
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natural language processingRecord type:
paper
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Ass
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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: Does scaling the size of the source language dataset mitigate the negative effects of annotation projection noise on cross-lingual NER F1 scores in low-resource target languages?
Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.
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