2022
EMNLP
EMNLP 2022
Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models
Abstract
AbstractMost 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— zero-shot cross-language transfer
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Paradigms > Transfer Learning
Deep Learning > Learning Types > Zero-Shot Learning
Natural Language Processing > Applications > Entity Linking