This paper addresses the task of highly structured output prediction in the contexts of, here, source code generation. The authors propose a two-step process for this, consisting of a retrieval model that retrieves the closest seen training instance to the test instance, and an edit model, that edits the retrieved training instance. Quality: - The authors approach the relatively recently proposed task of generating Python source code. Unlike others, they work in a setting with less supervision, where they assume no access to the Abstract Syntax Tree. I like this idea and the proposed model a lot and can see how it would be useful beyond the immediate scope of the paper.