In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce ReCode, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.

使用基于子树检索的方法来生成程序源代码。通过动态规划的句子相似性评分方法检索与输入句子相似的句子，提取建立相应抽象语法树的动作序列的n-gram，并增加导致检索到的n-gram动作子树出现在预测代码中的动作的概率。在两个代码生成任务中，ReCode方法提高了高达2.6 BLEU的性能。

基于检索的神经代码生成