Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data
Ivan Vulić, Marie‐Francine Moens · 2014
We propose the first probabilistic approach to modeling cross-lingual semantic sim-ilarity (CLSS) in context which requires only comparable data. The approach re-lies on an idea of projecting words and sets of words into a shared latent semantic space spanned by language-pair indepen-dent 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 re-sources. Word meaning is represented as a probability distribution over the latent concepts, and a change in meaning is rep-resented as a change in the distribution over these latent concepts. We present new models that modulate the isolated out-of-context word representations with contex-tual 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. 1