Iterative text revision improves text quality by fixing grammatical errors,
rephrasing for better readability or contextual appropriateness, or
reorganizing sentence structures throughout a document. Most recent research
has focused on understanding and classifying different types of edits in the
iterative revision process from human-written text instead of building accurate
and robust systems for iterative text revision. In this work, we aim to build
an end-to-end text revision system that can iteratively generate helpful edits
by explicitly detecting editable spans (where-to-edit) with their corresponding
edit intents and then instructing a revision model to revise the detected edit
spans. Leveraging datasets from other related text editing NLP tasks, combined
with the specification of editable spans, leads our system to more accurately
model the process of iterative text refinement, as evidenced by empirical
results and human evaluations. Our system significantly outperforms previous
baselines on our text revision tasks and other standard text revision tasks,
including grammatical error correction, text simplification, sentence fusion,
and style transfer. Through extensive qualitative and quantitative analysis, we
make vital connections between edit intentions and writing quality, and better
computational modeling of iterative text revisions.

本论文旨在构建一个端到端的文本修订系统，该系统可以通过显式检测可编辑范围及其对应的编辑意图并指导修订模型来迭代生成有用的编辑，从而更准确地模拟迭代文本修订的过程，并在我们的文本修订任务和其他标准文本修订任务上显著优于以前的基线，包括语法错误修正、文本简化、句子融合和风格转换。