Cross-Lingual Named Entity Recognition Method Based on Multi-Channel and Multi-Teacher Models

Currently, cross-lingual named entity recognition tasks primarily rely on knowledge distillation as a key technique. The design of diverse teacher models aims to guide the training of the target student model, effectively addressing the scarcity of target language data. However, existing methods often overemphasize the design of teacher models, overlooking the crucial significance of high-quality hard-label data. To address this issue, this paper proposes a multi-channel, multi-teacher cross-lingual named entity recognition approach (TSH-MC), comprising a translation teacher, a similarity teacher, and a hard-label screening module, with the objective of enhancing the training effectiveness of the target model. To fully exploit intermediate layer information, we introduce multi-channel knowledge distillation techniques, facilitating information exchange between the intermediate layers of teacher and student models, resulting in performance improvement. The proposed TSH-MC method has been experimentally validated on six languages from the CoNLL2002, CoNLL2003, and WikiAnn datasets, successfully demonstrating its effectiveness.