Junzhe Liang


2024

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Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization
Junzhe Liang | Haifeng Sun | Zirui Zhuang | Qi Qi | Jingyu Wang | Jianxin Liao
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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.