  We introduce the problem of learning distributed representations of edits. By
combining a "neural editor" with an "edit encoder", our models learn to
represent the salient information of an edit and can be used to apply edits to
new inputs. We experiment on natural language and source code edit data. Our
evaluation yields promising results that suggest that our neural network models
learn to capture the structure and semantics of edits. We hope that this
interesting task and data source will inspire other researchers to work further
on this problem.
