Improving Neural Knowledge Base Completion with Cross-Lingual Projections
Patrick Klein, Simone Paolo Ponzetto, Goran Glavaš · 2017
In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion.We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples.We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings).We then train the network with triples from the cross-lingually augmented knowledge base.Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.