@inproceedings{a6bcb39269384af2a0fecf755f135b79,
title = "Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking",
abstract = "Cross-language entity linking grounds mentions written in several languages to a monolingual knowledge base. We use a simple neural ranking architecture for this task that uses multilingual BERT representations of both the mention and the context as input, so as to explore the ability of a transformer model to perform well on this task. We find that the multilingual ability of BERT leads to good performance in monolingual and multilingual settings. Furthermore, we explore zero-shot language transfer and find surprisingly robust performance. We conduct several analyses to identify the sources of performance degradation in the zero-shot setting. Results indicate that while multilingual transformer models transfer well between languages, issues remain in disambiguating similar entities unseen in training.",
author = "Elliot Schumacher and James Mayfield and Mark Dredze",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 ; Conference date: 01-08-2021 Through 06-08-2021",
year = "2021",
doi = "10.18653/v1/2021.findings-acl.52",
language = "English (US)",
series = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "583--595",
editor = "Chengqing Zong and Fei Xia and Wenjie Li and Roberto Navigli",
booktitle = "Findings of the Association for Computational Linguistics",
address = "United States",
}