  The ability to match pieces of code to their corresponding natural language
descriptions and vice versa is fundamental for natural language search
interfaces to software repositories. In this paper, we propose a novel
multi-perspective cross-lingual neural framework for code--text matching,
inspired in part by a previous model for monolingual text-to-text matching, to
capture both global and local similarities. Our experiments on the CoNaLa
dataset show that our proposed model yields better performance on this
cross-lingual text-to-code matching task than previous approaches that map code
and text to a single joint embedding space.
