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arXiv · 2211.08104

DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition

Jiali Zeng·Yufan Jiang·Yongjing Yin·Xu Wang·Binghuai Lin·Yunbo Cao
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Abstract

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a {\it dual-teaching} manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER. Code is available at https://github.com/lemon0830/dualNER.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao. 2022-11-15. DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition. https://arxiv.org/abs/2211.08104

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