  This paper proposes a modularized sense induction and representation learning
model that jointly learns bilingual sense embeddings that align well in the
vector space, where the cross-lingual signal in the English-Chinese parallel
corpus is exploited to capture the collocation and distributed characteristics
in the language pair. The model is evaluated on the Stanford Contextual Word
Similarity (SCWS) dataset to ensure the quality of monolingual sense
embeddings. In addition, we introduce Bilingual Contextual Word Similarity
(BCWS), a large and high-quality dataset for evaluating cross-lingual sense
embeddings, which is the first attempt of measuring whether the learned
embeddings are indeed aligned well in the vector space. The proposed approach
shows the superior quality of sense embeddings evaluated in both monolingual
and bilingual spaces.
