Abstract
Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.- Anthology ID:
- 2022.mrl-1.4
- Volume:
- Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–51
- Language:
- URL:
- https://aclanthology.org/2022.mrl-1.4
- DOI:
- 10.18653/v1/2022.mrl-1.4
- Cite (ACL):
- Elliot Schumacher, James Mayfield, and Mark Dredze. 2022. Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models. In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 38–51, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models (Schumacher et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.mrl-1.4.pdf