@inproceedings{huang2021cosqa,
author = {Huang, Junjie and Tang, Duyu and Shou (寿林钧), Linjun and Gong (YIMING), Ming and Xu, Ke and Jiang (姜大昕), Daxin and Zhou, Ming and Duan, Nan},
title = {CoSQA: 20,000+ Web Queries for Code Search and Question Answering},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {May},
abstract = {Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce the CoSQA this http URL includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance query-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1%, and incorporating CoCLR brings a further improvement of 10.5%.},
url = {http://approjects.co.za/?big=en-us/research/publication/cosqa-20-000-web-queries-for-code-search-and-question-answering/},
}