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

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