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
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 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.- Anthology ID:
- 2022.findings-emnlp.132
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1837–1843
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.132
- DOI:
- 10.18653/v1/2022.findings-emnlp.132
- Cite (ACL):
- Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, and Yunbo Cao. 2022. DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1837–1843, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (Zeng et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.132.pdf