Publication
ACL 2020
Conference paper

A Multi-Perspective Architecture for Semantic Code Search

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Abstract

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 multiperspective 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.

Date

05 Jul 2020

Publication

ACL 2020

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