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Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words indoi:10.18653/v1/d18-1033 dblp:conf/emnlp/QiLSZX018 fatcat:tacr73frbvhp3muffmlhrjwkoa