Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization
Junzhe Liang, Haifeng Sun, Zirui Zhuang, Qi Qi, Jingyu Wang, Jianxin Liao
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.- Anthology ID:
- 2024.lrec-main.448
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 5006–5017
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.448
- DOI:
- Cite (ACL):
- Junzhe Liang, Haifeng Sun, Zirui Zhuang, Qi Qi, Jingyu Wang, and Jianxin Liao. 2024. Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5006–5017, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization (Liang et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.448.pdf