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    <abstract>Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia’s interlanguage links and thus suffer when the foreign language’s Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages, QuEL shows an average increase of 25% in gold candidate recall and of 13% in end-to-end linking accuracy over state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T Design Challenges in Low-resource Cross-lingual Entity Linking
%A Fu, Xingyu
%A Shi, Weijia
%A Yu, Xiaodong
%A Zhao, Zian
%A Roth, Dan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F fu-etal-2020-design
%X Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia’s interlanguage links and thus suffer when the foreign language’s Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages, QuEL shows an average increase of 25% in gold candidate recall and of 13% in end-to-end linking accuracy over state-of-the-art baselines.
%R 10.18653/v1/2020.emnlp-main.521
%U https://aclanthology.org/2020.emnlp-main.521
%U https://doi.org/10.18653/v1/2020.emnlp-main.521
%P 6418-6432

Markdown (Informal)

[Design Challenges in Low-resource Cross-lingual Entity Linking](https://aclanthology.org/2020.emnlp-main.521) (Fu et al., EMNLP 2020)

• Design Challenges in Low-resource Cross-lingual Entity Linking (Fu et al., EMNLP 2020)

ACL

• Xingyu Fu, Weijia Shi, Xiaodong Yu, Zian Zhao, and Dan Roth. 2020. Design Challenges in Low-resource Cross-lingual Entity Linking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6418–6432, Online. Association for Computational Linguistics.