CODEP: Grammatical Seq2Seq Model for General-Purpose Code Generation

General-purpose code generation aims to automatically convert the natural language (NL) description to code snippets in a general-purpose programming language (GPL) like Python. Intrinsically, code generation is a special type of text generation that generates well-formed text, i.e., code. However, existing sequence-to-sequence (Seq2Seq) approaches generate the GPL code neglecting the grammar rules. To this end, in this paper, we make the first attempt to consider grammatical Seq2Seq models for general-purpose code generation and propose CODEP, a grammatical Seq2Seq code generation framework equipped with a Pushdown automaton (PDA) module. In the training stage, CODEP additionally incorporates the state representation and the state prediction task, which leverages PDA states to help CODEP comprehend the parsing process of the PDA module. In the inference stage, CODEP generates well-formed code with the PDA module and the joint prediction of PDA states. Furthermore, the PDA module can be directly applied to Seq2Seq models without training to ensure the grammatical correctness of the generated code. To evaluate the effectiveness of our proposed method, we construct the DPA for the most popular GPL Python and conduct extensive experiments on four benchmark datasets. The experimental results demonstrate the superiority of CODEP compared to the state-of-the-art approaches without pre-training, and the DPA module also achieves significant improvements on the pre-trained models.