Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking
Elliot Schumacher, James Mayfield, Mark H. Dredze · 2021
Cross-language entity linking grounds mentions written in several languages to a monolingual knowledge base.We use a simple neural ranking architecture for this task that uses multilingual BERT representations of both the mention and the context as input, so as to explore the ability of a transformer model to perform well on this task.We find that the multilingual ability of BERT leads to good performance in monolingual and multilingual settings.Furthermore, we explore zero-shot language transfer and find surprisingly robust performance.We conduct several analyses to identify the sources of performance degradation in the zero-shot setting.Results indicate that while multilingual transformer models transfer well between languages, issues remain in disambiguating similar entities unseen in training.