论文标题
语义代码搜索的多镜头体系结构
A Multi-Perspective Architecture for Semantic Code Search
论文作者
论文摘要
将代码与其相应的自然语言描述匹配的能力，反之亦然，对于软件存储库的自然语言搜索接口至关重要。在本文中，我们提出了一个新型的多角度跨语义神经框架，用于代码 - 文本匹配，部分灵感来自以前单语文本到文本匹配的模型，以捕获全球和本地的相似性。我们在CONALA数据集上的实验表明，与以前的映射代码和文本为单个关节嵌入空间的方法相比，我们提出的模型在此跨语义文本对代码匹配任务上产生更好的性能。
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.
