Semantic Scaffolds for Pseudocode-to-Code Generation
Ruiqi Zhong, Mitchell E. Stern, Dan Klein · 2020
We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program.By first searching over plausible scaffolds then using these as constraints for a beam search over programs, we achieve better coverage of the search space when compared with existing techniques.We apply our hierarchical search method to the SPoC dataset for pseudocodeto-code generation, in which we are given line-level natural language pseudocode annotations and aim to produce a program satisfying execution-based test cases.By using semantic scaffolds during inference, we achieve a 10% absolute improvement in top-100 accuracy over the previous state-of-the-art.Additionally, we require only 11 candidates to reach the top-3000 performance of the previous best approach when tested against unseen problems, demonstrating a substantial improvement in efficiency.