A Retrieve-and-Edit Framework for Predicting Structured Outputs

Understand

For the task of generating complex outputs such as source code, editing existing outputs can be easier than generating complex outputs from scratch.

• With this motivation, we propose an approach that first retrieves a training example based on the input (e.g., natural language description) and then edits it to the desired output (e.g., code).
• Our contribution is a computationally efficient method for learning a retrieval model that embeds the input in a task-dependent way without relying on a hand-crafted metric or incurring the expense of jointly training the retriever with the editor.
• Our retrieve-and-edit framework can be applied on top of any base model.