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The Ethereal
Learning to Represent Edits
October 31, 2018 ยท Declared Dead ยท ๐ International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
arXiv ID
1810.13337
Category
cs.LG: Machine Learning
Cross-listed
cs.SE,
stat.ML
Citations
120
Venue
International Conference on Learning Representations
Last Checked
4 months ago
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.
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