  In contemporary machine learning approaches to bilingual lexicon induction
(BLI), a model learns a mapping between the embedding spaces of a language
pair. Recently, retrieve-and-rank approach to BLI has achieved state of the art
results on the task. However, the problem remains challenging in low-resource
settings, due to the paucity of data. The task is complicated by factors such
as lexical variation across languages. We argue that the incorporation of
additional lexical information into the recent retrieve-and-rank approach
should improve lexicon induction. We demonstrate the efficacy of our proposed
approach on XLING, improving over the previous state of the art by an average
of 2\% across all language pairs.
