Learning to Represent Edits
This work addresses the challenge of representing edits for researchers in machine learning and AI, but it is incremental as it builds on existing neural methods without a major breakthrough.
The paper tackles the problem of learning distributed representations of edits by combining a neural editor and edit encoder, achieving promising results in capturing structure and semantics on natural language and source code data.
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.