  Cross-lingual entity linking (XEL) is the task of finding referents in a
target-language knowledge base (KB) for mentions extracted from source-language
texts. The first step of (X)EL is candidate generation, which retrieves a list
of plausible candidate entities from the target-language KB for each mention.
Approaches based on resources from Wikipedia have proven successful in the
realm of relatively high-resource languages (HRL), but these do not extend well
to low-resource languages (LRL) with few, if any, Wikipedia pages. Recently,
transfer learning methods have been shown to reduce the demand for resources in
the LRL by utilizing resources in closely-related languages, but the
performance still lags far behind their high-resource counterparts. In this
paper, we first assess the problems faced by current entity candidate
generation methods for low-resource XEL, then propose three improvements that
(1) reduce the disconnect between entity mentions and KB entries, and (2)
improve the robustness of the model to low-resource scenarios. The methods are
simple, but effective: we experiment with our approach on seven XEL datasets
and find that they yield an average gain of 16.9% in Top-30 gold candidate
recall, compared to state-of-the-art baselines. Our improved model also yields
an average gain of 7.9% in in-KB accuracy of end-to-end XEL.
