  We propose a new formulation for multilingual entity linking, where
language-specific mentions resolve to a language-agnostic Knowledge Base. We
train a dual encoder in this new setting, building on prior work with improved
feature representation, negative mining, and an auxiliary entity-pairing task,
to obtain a single entity retrieval model that covers 100+ languages and 20
million entities. The model outperforms state-of-the-art results from a far
more limited cross-lingual linking task. Rare entities and low-resource
languages pose challenges at this large-scale, so we advocate for an increased
focus on zero- and few-shot evaluation. To this end, we provide Mewsli-9, a
large new multilingual dataset (http://goo.gle/mewsli-dataset) matched to our
setting, and show how frequency-based analysis provided key insights for our
model and training enhancements.
