陈俊帆
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陈俊帆
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论文
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition
发布时间：2025-10-22点击次数：
发表刊物：
Annual Meeting of the Association for Computational Linguistics (ACL), CCF-A
摘要：
Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages. Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer. However, existing cross-lingual distillation models merely consider the potential transferability between two identical single tasks across both domains. Other possible auxiliary tasks to improve the learning performance have not been fully investigated. In this study, based on the knowledge distillation framework and multitask learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain. Specifically, an entity recognizer and a similarity evaluator are first trained in parallel as two teachers from the source domain. Then, two tasks in the student model are supervised by these teachers simultaneously. Empirical studies on the three datasets across 7 different languages confirm the effectiveness of the proposed model.
合写作者：
Zhuoran Li,胡春明, Xiaohui Guo,陈俊帆, Wenyi Qin,张日崇
论文类型：
国际学术会议
页面范围：
170-179
是否译文：
否
发表时间：
2022-01-01