Python Code Generation by Asking Clarification Questions

TL;DR

This paper proposes a new approach to code generation from natural language by incorporating clarification questions, demonstrating that this method improves the accuracy of generated code and introducing a new dataset for this task.

Contribution

It introduces a novel setup for code generation that involves asking clarification questions, along with a new dataset, CodeClarQA, to facilitate research in this area.

Findings

Clarification questions improve code generation accuracy

Pretrained models perform better with clarification-based interactions

The new dataset enables research on when and what questions to ask

Abstract

Code generation from text requires understanding the user's intent from a natural language description and generating an executable code snippet that satisfies this intent. While recent pretrained language models demonstrate remarkable performance for this task, these models fail when the given natural language description is under-specified. In this work, we introduce a novel and more realistic setup for this task. We hypothesize that the under-specification of a natural language description can be resolved by asking clarification questions. Therefore, we collect and introduce a new dataset named CodeClarQA containing pairs of natural language descriptions and code with created synthetic clarification questions and answers. The empirical results of our evaluation of pretrained language model performance on code generation show that clarifications result in more precisely generated code, as shown by the substantial improvement of model performance in all evaluation metrics. Alongside this, our task and dataset introduce new challenges to the community, including when and what clarification questions should be asked. Our code and dataset are available on GitHub.