  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.
