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Learning to Represent Edits

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arxiv 1810.13337 v2 pith:HDYJZCZV submitted 2018-10-31 cs.LG cs.SEstat.ML

Pengcheng Yin , Graham Neubig , Miltiadis Allamanis , Marc Brockschmidt , Alexander L. Gaunt This is my paper · ORCID
classification cs.LGcs.SEstat.ML
keywords editseditdatalearnlearningmodelsneuralproblem
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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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