  We present mGENRE, a sequence-to-sequence system for the Multilingual Entity
Linking (MEL) problem -- the task of resolving language-specific mentions to a
multilingual Knowledge Base (KB). For a mention in a given language, mGENRE
predicts the name of the target entity left-to-right, token-by-token in an
autoregressive fashion. The autoregressive formulation allows us to effectively
cross-encode mention string and entity names to capture more interactions than
the standard dot product between mention and entity vectors. It also enables
fast search within a large KB even for mentions that do not appear in mention
tables and with no need for large-scale vector indices. While prior MEL works
use a single representation for each entity, we match against entity names of
as many languages as possible, which allows exploiting language connections
between source input and target name. Moreover, in a zero-shot setting on
languages with no training data at all, mGENRE treats the target language as a
latent variable that is marginalized at prediction time. This leads to over 50%
improvements in average accuracy. We show the efficacy of our approach through
extensive evaluation including experiments on three popular MEL benchmarks
where mGENRE establishes new state-of-the-art results. Code and pre-trained
models at https://github.com/facebookresearch/GENRE.
