Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data

We propose the first probabilistic approach to modeling cross-lingual semantic similarity (CLSS) in context which requires only comparable data. The approach relies on an idea of projecting words and sets of words into a shared latent semantic space spanned by language-pair independent latent semantic concepts (e.g., crosslingual topics obtained by a multilingual topic model). These latent cross-lingual concepts are induced from a comparable corpus without any additional lexical resources. Word meaning is represented as a probability distribution over the latent concepts, and a change in meaning is represented as a change in the distribution over these latent concepts. We present new models that modulate the isolated out-ofcontext word representations with contextual knowledge. Results on the task of suggesting word translations in context for 3 language pairs reveal the utility of the proposed contextualized models of crosslingual semantic similarity.