Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking

Elliot Schumacher; James Mayfield; Mark Dredze

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arXiv · 2010.09828

Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking

Elliot Schumacher·James Mayfield·Mark Dredze

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Abstract

Cross-language entity linking grounds mentions in multiple languages to a single-language knowledge base. We propose a neural ranking architecture for this task that uses multilingual BERT representations of the mention and the context in a neural network. We find that the multilingual ability of BERT leads to robust performance in monolingual and multilingual settings. Furthermore, we explore zero-shot language transfer and find surprisingly robust performance. We investigate the zero-shot degradation and find that it can be partially mitigated by a proposed auxiliary training objective, but that the remaining error can best be attributed to domain shift rather than language transfer.

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BibTeX RIS

Elliot Schumacher, James Mayfield, Mark Dredze. 2020-10-19. Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking. https://arxiv.org/abs/2010.09828

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