一句话：讨论深度学习用于自然语言代码搜索：将代码与查询嵌入向量空间，并区分无监督与有监督嵌入学习。
论文关注深度学习在自然语言代码搜索中的应用。近期多项工作共同采用的做法是：把代码和自然语言查询嵌入到实向量空间，再用向量距离来近似二者之间的语义相关性。学习此类嵌入主要有两条路线：无监督方法仅利用代码示例语料；有监督方法则利用代码与自然语言描述对齐的语料。监督学习的目的是让某个查询与其期望代码片段的嵌入更相似，从而提高检索效果。摘要未展开具体模型、数据集与实验结论。
There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of embedding code and natural language queries into real vectors and then using vector distance to approximate semantic correlation between code and the query. Multiple approaches exist for learning these embeddings, including unsupervised techniques, which rely only on a corpus of code examples, and supervised techniques, which use an aligned corpus of paired code and natural language descriptions. The goal of this supervision is to produce embeddings that are more similar for a query and the corresponding desired code snippet.