Entity Linking in 100 Languages
Jan A. Botha, Zifei Shan, Daniel Gillick · 2020
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 1 matched to our setting, and show how frequency-based analysis provided key insights for our model and training enhancements.