Adversarial Training for Robust Cross-Lingual NER in Low-Resource Settings

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: Can adversarial training during the projection-based data transfer step improve the robustness of cross-lingual NER models against distribution shifts in low-resource language datasets, as evaluated by accuracy on the MLQA benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.