  We construct a multilingual common semantic space based on distributional
semantics, where words from multiple languages are projected into a shared
space to enable knowledge and resource transfer across languages. Beyond word
alignment, we introduce multiple cluster-level alignments and enforce the word
clusters to be consistently distributed across multiple languages. We exploit
three signals for clustering: (1) neighbor words in the monolingual word
embedding space; (2) character-level information; and (3) linguistic properties
(e.g., apposition, locative suffix) derived from linguistic structure knowledge
bases available for thousands of languages. We introduce a new
cluster-consistent correlational neural network to construct the common
semantic space by aligning words as well as clusters. Intrinsic evaluation on
monolingual and multilingual QVEC tasks shows our approach achieves
significantly higher correlation with linguistic features than state-of-the-art
multi-lingual embedding learning methods do. Using low-resource language name
tagging as a case study for extrinsic evaluation, our approach achieves up to
24.5\% absolute F-score gain over the state of the art.
