Self-training in Multi-source Cross-lingual NER for Low-resource Languages

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 incorporating self-training with unlabeled target language data in multi-source cross-lingual NER frameworks improve F1 scores on CoNLL-2003 benchmarks compared to XLM-R-based models when evaluated on low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.