  Entity linking -- the task of identifying references in free text to relevant
knowledge base representations -- often focuses on single languages. We
consider multilingual entity linking, where a single model is trained to link
references to same-language knowledge bases in several languages. We propose a
neural ranker architecture, which leverages multilingual transformer
representations of text to be easily applied to a multilingual setting. We then
explore how a neural ranker trained in one language (e.g. English) transfers to
an unseen language (e.g. Chinese), and find that while there is a consistent
but not large drop in performance. How can this drop in performance be
alleviated? We explore adding an adversarial objective to force our model to
learn language-invariant representations. We find that using this approach
improves recall in several datasets, often matching the in-language
performance, thus alleviating some of the performance loss occurring from
zero-shot transfer.
