Entity Linking in 100 Languages
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 tas...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
05.11.2020
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Subjects | |
Online Access | Get full text |
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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,
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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DOI: | 10.48550/arxiv.2011.02690 |