  Bilingual Lexicon Induction (BLI) aims to map words in one language to their
translations in another, and is typically through learning linear projections
to align monolingual word representation spaces. Two classes of word
representations have been explored for BLI: static word embeddings and
contextual representations, but there is no studies to combine both. In this
paper, we propose a simple yet effective mechanism to combine the static word
embeddings and the contextual representations to utilize the advantages of both
paradigms. We test the combination mechanism on various language pairs under
the supervised and unsupervised BLI benchmark settings. Experiments show that
our mechanism consistently improves performances over robust BLI baselines on
all language pairs by averagely improving 3.2 points in the supervised setting,
and 3.1 points in the unsupervised setting.
