%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