Design Challenges in Low-resource Cross-lingual Entity Linking

TL;DR

This paper analyzes the limitations of current low-resource cross-lingual entity linking methods and introduces QuEL, a zero-shot system that improves candidate recall and linking accuracy by leveraging search engine query logs across 25 languages.

Contribution

It identifies the reliance on Wikipedia links as a key limitation and proposes a novel zero-shot approach using search logs to enhance low-resource language entity linking.

Findings

QuEL increases candidate recall by 25% on average.

QuEL improves end-to-end linking accuracy by 13%.

Using search logs effectively addresses low-resource language challenges.

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.