@inproceedings{schumacher-etal-2022-zero,
    title = "Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models",
    author = "Schumacher, Elliot  and
      Mayfield, James  and
      Dredze, Mark",
    editor = {Ataman, Duygu  and
      Gonen, Hila  and
      Ruder, Sebastian  and
      Firat, Orhan  and
      G{\"u}l Sahin, G{\"o}zde  and
      Mirzakhalov, Jamshidbek},
    booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.mrl-1.4/",
    doi = "10.18653/v1/2022.mrl-1.4",
    pages = "38--51",
    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."
}