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.

本文提出了一种通过多步编辑的方式，建立生成序列的生成模型，以及训练该模型的神经网络，并基于多步编辑提出了基础结果和度量标准。实验结果表明，所提出的模型在相关下游任务上的表现优于以往单步编辑模型的表现。