Multilingual End to End Entity Linking
Mikhail Plekhanov Nora Kassner Kashyap Popat Louis Martin Simone Merello Borislav Kozlovskii Frédéric A. Dreyer Nicola Cancedda
Entity Linking is one of the most common Natural Language Processing tasks in
practical applications, but so far efficient end-to-end solutions with
multilingual coverage have been lacking, leading to complex model stacks. To
fill this gap, we release and open source BELA, the first fully end-to-end
multilingual entity linking model that efficiently detects and links entities
in texts in any of 97 languages. We provide here a detailed description of the
model and report BELA's performance on four entity linking datasets covering
high- and low-resource languages.