  Existing approaches to mapping-based cross-lingual word embeddings are based
on the assumption that the source and target embedding spaces are structurally
similar. The structures of embedding spaces largely depend on the co-occurrence
statistics of each word, which the choice of context window determines. Despite
this obvious connection between the context window and mapping-based
cross-lingual embeddings, their relationship has been underexplored in prior
work. In this work, we provide a thorough evaluation, in various languages,
domains, and tasks, of bilingual embeddings trained with different context
windows. The highlight of our findings is that increasing the size of both the
source and target window sizes improves the performance of bilingual lexicon
induction, especially the performance on frequent nouns.
