Learning to Model Editing Processes
Machel Reid, Graham Neubig · 2022
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 multi-step 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. 1