@inproceedings{xu-etal-2020-incorporating,
title = "Incorporating External Knowledge through Pre-training for Natural Language to Code Generation",
author = "Xu, Frank F. and
Jiang, Zhengbao and
Yin, Pengcheng and
Vasilescu, Bogdan and
Neubig, Graham",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.538",
doi = "10.18653/v1/2020.acl-main.538",
pages = "6045--6052",
abstract = "Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2{\%} absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at \url{https://github.com/neulab/external-knowledge-codegen}.",
}
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<abstract>Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.</abstract>
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%0 Conference Proceedings
%T Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
%A Xu, Frank F.
%A Jiang, Zhengbao
%A Yin, Pengcheng
%A Vasilescu, Bogdan
%A Neubig, Graham
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-incorporating
%X Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.
%R 10.18653/v1/2020.acl-main.538
%U https://aclanthology.org/2020.acl-main.538
%U https://doi.org/10.18653/v1/2020.acl-main.538
%P 6045-6052
Markdown (Informal)
[Incorporating External Knowledge through Pre-training for Natural Language to Code Generation](https://aclanthology.org/2020.acl-main.538) (Xu et al., ACL 2020)
ACL