Year: 2020
Type: conference-paper
Abstract: 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.
Authors: Jan A. Botha, Zifei Shan, Daniel Gillick
Institution: Google (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3096813544