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For the task of generating complex outputs such as source code, editingexisting outputs can be easier than generating complex outputs from scratch.With this motivation, we propose an approach that first retrieves a trainingexample based on the input (e.g., natural language description) and then editsit to the desired output (e.g., code). Our contribution is a computationallyefficient method for learning a retrieval model that embeds the input in atask-dependent way without relying on a hand-crafted metric or incurring theexpense of jointly training the retriever with the editor. Ourretrieve-and-edit framework can be applied on top of any base model. We showthat on a new autocomplete task for GitHub Python code and the Hearthstonecards benchmark, retrieve-and-edit significantly boosts the performance of avanilla sequence-to-sequence model on both tasks.

A Retrieve-and-Edit Framework for Predicting Structured Outputs