Entity Linking in 100 Languages

• URL: http://arxiv.org/abs/2011.02690v1
• Date: Thu, 5 Nov 2020 07:28:35 GMT
• Title: Entity Linking in 100 Languages
• Authors: Jan A. Botha, Zifei Shan, Daniel Gillick
• Abstract summary: 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. The model outperforms state-of-the-art results from a far more limited cross-lingual linking task.
• Score: 3.2099113524828513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (http://goo.gle/mewsli-dataset) matched to our setting, and show how frequency-based analysis provided key insights for our model and training enhancements.

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