Code Generation from Natural Language with Less Prior and More Monolingual Data

TL;DR

This paper demonstrates that a transformer-based seq2seq model, trained on monolingual data, can achieve state-of-the-art performance in code generation from natural language without relying heavily on task-specific priors.

Contribution

It shows that minimal task-specific bias combined with large monolingual data can produce competitive code generation models, simplifying the development process.

Findings

Achieved 81.03% exact match accuracy on Django

Reached 32.57 BLEU score on CoNaLa

Outperformed previous models in code generation tasks

Abstract

Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application usually need additional prior knowledge to be built into the architecture or algorithm. The increased dependency on human experts hinders automation and raises the development and maintenance costs in practice. This work investigates whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. By exploiting a relatively sizeable monolingual corpus of the target programming language, which is cheap to mine from the web, we achieved 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. Both are SOTA to the best of our knowledge. This positive evidence highlights a potentially easier path toward building accurate semantic parsers in practice.