Alignment Metric Selection in Adversarial Cross-Lingual NER: Convergence and Accuracy Trade-offs

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: To what extent does the choice of alignment metric (e.g., Wasserstein distance, KL divergence) in adversarial cross-lingual NER models affect convergence speed and entity recognition accuracy across low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.