Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition

Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, Jian Wang


Abstract
Chinese Named Entity Recognition (NER) has continued to attract research attention. However, most existing studies only explore the internal features of the Chinese language but neglect other lingual modal features. Actually, as another modal knowledge of the Chinese language, English contains rich prompts about entities that can potentially be applied to improve the performance of Chinese NER. Therefore, in this study, we explore the bilingual enhancement for Chinese NER and propose a unified bilingual interaction module called the Adapted Cross-Transformers with Global Sparse Attention (ACT-S) to capture the interaction of bilingual information. We utilize a model built upon several different ACT-Ss to integrate the rich English information into the Chinese representation. Moreover, our model can learn the interaction of information between bilinguals (inter-features) and the dependency information within Chinese (intra-features). Compared with existing Chinese NER methods, our proposed model can better handle entities with complex structures. The English text that enhances the model is automatically generated by machine translation, avoiding high labour costs. Experimental results on four well-known benchmark datasets demonstrate the effectiveness and robustness of our proposed model.
Anthology ID:
2022.coling-1.176
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2024–2033
Language:
URL:
https://aclanthology.org/2022.coling-1.176
DOI:
Bibkey:
Cite (ACL):
Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, and Jian Wang. 2022. Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2024–2033, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (Ning et al., COLING 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.coling-1.176.pdf
Data
Weibo NER