  Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in
any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most
languages is challenging, owing to limited availability of resources as
supervision. We address this challenge by developing the first XEL approach
that combines supervision from multiple languages jointly. This enables our
approach to: (a) augment the limited supervision in the target language with
additional supervision from a high-resource language (like English), and (b)
train a single entity linking model for multiple languages, improving upon
individually trained models for each language. Extensive evaluation on three
benchmark datasets across 8 languages shows that our approach significantly
improves over the current state-of-the-art. We also provide analyses in two
limited resource settings: (a) zero-shot setting, when no supervision in the
target language is available, and in (b) low-resource setting, when some
supervision in the target language is available. Our analysis provides insights
into the limitations of zero-shot XEL approaches in realistic scenarios, and
shows the value of joint supervision in low-resource settings.
