@inproceedings{liang-etal-2024-distantly-supervised,
title = "Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization",
author = "Liang, Junzhe and
Sun, Haifeng and
Zhuang, Zirui and
Qi, Qi and
Wang, Jingyu and
Liao, Jianxin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.448",
pages = "5006--5017",
abstract = "Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code{---}programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method{'}s superiority over competitive baselines.",
}
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<abstract>Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code—programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method’s superiority over competitive baselines.</abstract>
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%0 Conference Proceedings
%T Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization
%A Liang, Junzhe
%A Sun, Haifeng
%A Zhuang, Zirui
%A Qi, Qi
%A Wang, Jingyu
%A Liao, Jianxin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F liang-etal-2024-distantly-supervised
%X Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code—programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method’s superiority over competitive baselines.
%U https://aclanthology.org/2024.lrec-main.448
%P 5006-5017
Markdown (Informal)
[Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization](https://aclanthology.org/2024.lrec-main.448) (Liang et al., LREC-COLING 2024)
ACL