Improving Zero-Shot Multi-Lingual Entity Linking

• URL: http://arxiv.org/abs/2104.08082v1
• Date: Fri, 16 Apr 2021 12:50:07 GMT
• Title: Improving Zero-Shot Multi-Lingual Entity Linking
• Authors: Elliot Schumacher, James Mayfield, and Mark Dredze
• Abstract summary: We consider multilingual entity linking, where a single model is trained to link references to same-language knowledge bases in several languages. We propose a neural ranker architecture, which leverages multilingual transformer representations of text to be easily applied to a multilingual setting. We find that using this approach improves recall in several datasets, often matching the in-language performance.
• Score: 14.502266106371433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity linking -- the task of identifying references in free text to relevant knowledge base representations -- often focuses on single languages. We consider multilingual entity linking, where a single model is trained to link references to same-language knowledge bases in several languages. We propose a neural ranker architecture, which leverages multilingual transformer representations of text to be easily applied to a multilingual setting. We then explore how a neural ranker trained in one language (e.g. English) transfers to an unseen language (e.g. Chinese), and find that while there is a consistent but not large drop in performance. How can this drop in performance be alleviated? We explore adding an adversarial objective to force our model to learn language-invariant representations. We find that using this approach improves recall in several datasets, often matching the in-language performance, thus alleviating some of the performance loss occurring from zero-shot transfer.

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