  Cross-lingual Hypernymy Detection involves determining if a word in one
language ("fruit") is a hypernym of a word in another language ("pomme" i.e.
apple in French). The ability to detect hypernymy cross-lingually can aid in
solving cross-lingual versions of tasks such as textual entailment and event
coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for
cross-lingual hypernymy detection, which learns sparse, bilingual word
embeddings based on dependency contexts. We show that BISPARSE-DEP can
significantly improve performance on this task, compared to approaches based
only on lexical context. Our approach is also robust, showing promise for
low-resource settings: our dependency-based embeddings can be learned using a
parser trained on related languages, with negligible loss in performance. We
also crowd-source a challenging dataset for this task on four languages --
Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly
available.
