title: Learning to Represent Edits
abstract: 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.
authors: Pengcheng Yin Graham Neubig Miltiadis Allamanis Marc Brockschmidt Alexander L. Gaunt
hIndex: Pengcheng Yin 19