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        <title>Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models
%A Schumacher, Elliot
%A Mayfield, James
%A Dredze, Mark
%Y Ataman, Duygu
%Y Gonen, Hila
%Y Ruder, Sebastian
%Y Firat, Orhan
%Y Gül Sahin, Gözde
%Y Mirzakhalov, Jamshidbek
%S Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F schumacher-etal-2022-zero
%X 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.
%R 10.18653/v1/2022.mrl-1.4
%U https://aclanthology.org/2022.mrl-1.4
%U https://doi.org/10.18653/v1/2022.mrl-1.4
%P 38-51

Markdown (Informal)

[Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models](https://aclanthology.org/2022.mrl-1.4) (Schumacher et al., MRL 2022)

• Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models (Schumacher et al., MRL 2022)

ACL

• Elliot Schumacher, James Mayfield, and Mark Dredze. 2022. Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 38–51, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.