Learning to Model Editing Processes

• URL: http://arxiv.org/abs/2205.12374v1
• Date: Tue, 24 May 2022 21:32:52 GMT
• Title: Learning to Model Editing Processes
• Authors: Machel Reid and Graham Neubig
• Abstract summary: We propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits.
• Score: 98.11448946134894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.

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