  Cross-lingual entity linking (XEL) grounds named entities in a source
language to an English Knowledge Base (KB), such as Wikipedia. XEL is
challenging for most languages because of limited availability of requisite
resources. However, much previous work on XEL has been on simulated settings
that actually use significant resources (e.g. source language Wikipedia,
bilingual entity maps, multilingual embeddings) that are unavailable in truly
low-resource languages. In this work, we first examine the effect of these
resource assumptions and quantify how much the availability of these resource
affects overall quality of existing XEL systems. Next, we propose three
improvements to both entity candidate generation and disambiguation that make
better use of the limited data we do have in resource-scarce scenarios. With
experiments on four extremely low-resource languages, we show that our model
results in gains of 6-23% in end-to-end linking accuracy.
