Towards zero-shot cross-lingual named entity disambiguation
作者:
Highlights:
• Novel zero-shot cross-lingual Named Entity Disambiguation approach.
• Robust system that does not require native prior probabilities.
• Purpose-built multilingual method outperforms generic models such as XLM-R.
• English is not necessarily the most effective training language for zero-shot.
• New dataset for Basque/English, which facilitates further research.
摘要
•Novel zero-shot cross-lingual Named Entity Disambiguation approach.•Robust system that does not require native prior probabilities.•Purpose-built multilingual method outperforms generic models such as XLM-R.•English is not necessarily the most effective training language for zero-shot.•New dataset for Basque/English, which facilitates further research.
论文关键词:Cross-lingual named entity disambiguation,Cross-lingual entity linking,Zero-shot learning,Transfer learning,Pre-trained language models,Low-resource languages
论文评审过程:Received 22 February 2021, Revised 29 June 2021, Accepted 30 June 2021, Available online 9 July 2021, Version of Record 20 July 2021.
论文官网地址:https://doi.org/10.1016/j.eswa.2021.115542