Probabilistic models of cross-lingual semantic similarity in context based on latent cross-lingual concepts induced from comparable data

摘要

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., cross lingual 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-of-context 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 cross lingual semantic similarity.