Robustness of Multi-Source Teacher-Student Learning to Domain Shift in Cross-Lingual NER Versus Pairwise Label Projection

Abstract

Abstract: To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limi

Research Question

Is multi-source teacher-student learning on unlabeled data more robust to domain shift in cross-lingual NER than pairwise label projection methods?

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