Retrieval-Based Neural Code Generation
Shirley Anugrah Hayati, Raphaël Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig · 2018
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 dynamicprogramming-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. 1