用代码概念图增强的多模态学习更好地建模编程世界
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
摘要：
近年来，由于基于最新模型架构的自然语言处理学习方法的设计，代码建模取得了巨大的进展。尽管如此，我们认为，目前的国家的最先进的没有足够的集中在充分的潜力，数据可能带来的学习过程中的软件工程。我们的愿景阐述了利用多模态学习方法来建模编程世界的想法。在本文中，我们探讨了我们的愿景，其目标的基础上的概念图的标识符的目的是利用高层次的领域概念之间的关系，通过特定的语言结构操纵的基本思想之一。特别是，我们建议通过与基于我们的概念图的图神经网络联合学习来增强现有的预训练语言模型。我们进行了初步的评估，显示使用简单的联合学习方法的代码搜索模型的有效性，并提示我们进一步研究我们的研究愿景。
英文摘要：
The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
