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.
@article{arxiv.2306.08896,
title = {Multilingual End to End Entity Linking},
author = {Mikhail Plekhanov and Nora Kassner and Kashyap Popat and Louis Martin and Simone Merello and Borislav Kozlovskii and Frédéric A. Dreyer and Nicola Cancedda},
journal= {arXiv preprint arXiv:2306.08896},
year = {2023}
}