A Multi-Perspective Architecture for Semantic Code Search

Rajarshi Haldar, Lingfei Wu, JinJun Xiong, Julia Hockenmaier


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 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.
Anthology ID:
2020.acl-main.758
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8563–8568
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.758
DOI:
10.18653/v1/2020.acl-main.758
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.758.pdf
Video:
 http://slideslive.com/38929341