  Most of the existing methods for bilingual word embedding only consider
shallow context or simple co-occurrence information. In this paper, we propose
a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived
from a maximum complete sub-graph of pointwise mutual information based graph
over bilingual corpus. In this way, we treat source and target words equally
and a separated bilingual projection processing that have to be used in most
existing works is not necessary any more. Several dimension reduction methods
are evaluated to summarize the BSC-word relationship. The proposed method is
evaluated on bilingual lexicon translation tasks and empirical results show
that bilingual sense embedding methods outperform existing bilingual word
embedding methods.
