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Iterative text revision improves text quality by fixing grammatical errors,rephrasing for better readability or contextual appropriateness, orreorganizing sentence structures throughout a document. Most recent researchhas focused on understanding and classifying different types of edits in theiterative revision process from human-written text instead of building accurateand robust systems for iterative text revision. In this work, we aim to buildan end-to-end text revision system that can iteratively generate helpful editsby explicitly detecting editable spans (where-to-edit) with their correspondingedit intents and then instructing a revision model to revise the detected editspans. Leveraging datasets from other related text editing NLP tasks, combinedwith the specification of editable spans, leads our system to more accuratelymodel the process of iterative text refinement, as evidenced by empiricalresults and human evaluations. Our system significantly outperforms previousbaselines 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, wemake vital connections between edit intentions and writing quality, and bettercomputational modeling of iterative text revisions.

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks