A multi-perspective architecture for semantic code search

Rajarshi Haldar; Lingfei Wu; Jinjun Xiong; Julia Hockenmaier

A multi-perspective architecture for semantic code search

Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	8563-8568
Number of pages	6
ISBN (Electronic)	9781952148255
State	Published - 2020
Event	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: Jul 5 2020 → Jul 10 2020

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/Territory	United States
City	Virtual, Online
Period	7/5/20 → 7/10/20

ASJC Scopus subject areas

Computer Science Applications
Linguistics and Language
Language and Linguistics

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Haldar, R., Wu, L., Xiong, J., & Hockenmaier, J. (2020). A multi-perspective architecture for semantic code search. In ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 8563-8568). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL).

A multi-perspective architecture for semantic code search. / Haldar, Rajarshi; Wu, Lingfei; Xiong, Jinjun et al.
ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020. p. 8563-8568 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Haldar, R, Wu, L, Xiong, J & Hockenmaier, J 2020, A multi-perspective architecture for semantic code search. in ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 8563-8568, 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Virtual, Online, United States, 7/5/20.

Haldar, Rajarshi ; Wu, Lingfei ; Xiong, Jinjun et al. / A multi-perspective architecture for semantic code search. ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020. pp. 8563-8568 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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AB - 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.

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A multi-perspective architecture for semantic code search

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