Code2Text: Dual Attention Syntax Annotation Networks for Structure-Aware Code Translation

Translating source code into natural language text helps people understand the computer program better and faster. Previous code translation methods mainly exploit human specified syntax rules. Since handcrafted syntax rules are expensive to obtain and not always available, a PL-independent automatic code translation method is much more desired. However, existing sequence translation methods generally regard source text as a plain sequence, which is not competent to capture the rich hierarchical characteristics inherently reside in the code. In this work, we exploit the abstract syntax tree (AST) that summarizes the hierarchical information of a code snippet to build a structure-aware code translation method. We propose a syntax annotation network called Code2Text to incorporate both source code and its AST into the translation. Our Code2Text features the dual encoders for the sequential input (code) and the structural input (AST) respectively. We also propose a novel dual-attention mechanism to guide the decoding process by accurately aligning the output words with both the tokens in the source code and the nodes in the AST. Experiments on a public collection of Python code demonstrate that Code2Text achieves better performance compared to several state-of-the-art methods, and the generation of Code2Text is accurate and human-readable.