Abstract
Entity linking aims to identify mentions from the text and link them to a knowledge base. Further, Multi-lingual Entity Linking (MEL) is a more challenging task, where the language-specific mentions need to be linked to a multi-lingual knowledge base. To tackle the MEL task, we propose a novel model that employs the merit of adversarial learning and few-shot learning to generalize the learning ability across languages. Specifically, we first randomly select a fraction of language-agnostic unlabeled data as the language signal to construct the language discriminator. Based on it, we devise a simple and effective adversarial learning framework with two characteristic branches, including an entity classifier and a language discriminator with adversarial training. Experimental results on two benchmark datasets indicate the excellent performance in few-shot learning and the effectiveness of the proposed adversarial learning framework.- Anthology ID:
- 2024.sighan-1.4
- Volume:
- Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Kam-Fai Wong, Min Zhang, Ruifeng Xu, Jing Li, Zhongyu Wei, Lin Gui, Bin Liang, Runcong Zhao
- Venues:
- SIGHAN | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 28–35
- Language:
- URL:
- https://aclanthology.org/2024.sighan-1.4
- DOI:
- Cite (ACL):
- Bingbing Wang, Bin Liang, Zhixin Bai, and Yongzhuo Ma. 2024. Adversarial Learning for Multi-Lingual Entity Linking. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 28–35, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Adversarial Learning for Multi-Lingual Entity Linking (Wang et al., SIGHAN-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.sighan-1.4.pdf