Understanding Iterative Revision from Human-Written Text

TL;DR

This paper introduces IteraTeR, a large-scale, multi-domain corpus of human-written, iteratively revised texts with annotated edit intentions, advancing understanding and modeling of the revision process across various writing contexts.

Contribution

It presents the first comprehensive, multi-domain corpus of annotated iterative revisions and demonstrates how incorporating edit intentions improves computational revision models.

Findings

Annotated edit intentions enhance model performance

IteraTeR covers diverse domains and revision granularities

Better understanding of revision quality and process

Abstract

Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, IteraTeR is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations. Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.