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• W2898951026 abstract "We introduce the problem of learning distributed representations of edits. By combining a editor with an 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." @default.
• W2898951026 created "2018-11-09" @default.
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• W2898951026 date "2018-10-31" @default.
• W2898951026 modified "2023-09-27" @default.
• W2898951026 title "Learning to Represent Edits" @default.
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• W2898951026 hasPublicationYear "2018" @default.
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