Learning to Represent Edits

Understand

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.