Understanding Iterative Revision from Human-Written Text
39128Authors
nan
Abstract
AbstractWriting 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 generalizes to a variety of domains, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and action-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.
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Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 3573 - 3590 May 22-27, 2022 c⃝2022 Association for Computational Linguistics Understanding Iterative Revision from Human-Written Text Wanyu Du1∗, Vipul Raheja2 , Dhruv Kumar2 , Zae Myung Kim3 , Melissa Lopez2 , Dongyeop Kang4 1University of Virginia, 2Grammarly, 3Univ. Grenoble Alpes, CNRS, LIG, 4University of Minnesota wd5jq@virginia.edu {vipul.raheja,dhruv.kumar,melissa.lopez}@grammarly.com zae-myung.kim@univ-grenoble-alpes.fr dongyeop@umn.edu Abstract Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revis- ing the text. Previous works on text revision have focused on defining edit intention tax- onomies within a single domain or develop- ing computational models with a single level of edit granularity, such as sentence-level ed- its, 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 partic- ular, ITERATER is collected based on a new framework to comprehensively model the iter- ative text revisions that generalize to various domains of formal writing, edit intentions, re- vision depths, and granularities. When we in- corporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations.1 Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, en- abling the creation of diverse corpora to sup- port computational modeling of iterative text revisions. 1 Introduction Writing is a complex and effortful cognitive task, where writers balance and orchestrate three distinct cognitive processes: planning, translation, and re- vising (Flower and Hayes, 1980). These processes can be hierarchical and recursive and can occur at any moment during writing. This work focuses on text revision as an essential part of writing (Scar- damalia, 1986). Revising text is a strategic, and adaptive process. It enables writers to deliberate over and organize their thoughts, find a better line of argument, learn afresh, and discover what was ∗This research was performed when Wanyu Du was in- terning at Grammarly. 1Code and dataset are available at https://github. com/vipulraheja/IteraTeR. Each comment was annotated by three different annotators, which achieved high inter-annotator agreement. The proposed annotation {process approach} CLARITY is also language and domain independent{, nevertheless, it was currently ap- plied for Brazilian Portuguese} MEANING-CHANGED . Each comment was annotated by three different annotators, {which and} COHERENCE achieved high inter-annotator agreement. The {new} MEANING-CHANGED proposed an- notation approach is also language and {domain independent, nevertheless, it was currentlydomain-independent (although it has been} CLARITY applied for Brazilian Por- tuguese{)} FLUENCY . Each comment was annotated by three different annota- tors {,} FLUENCY and achieved high inter-annotator agree- ment. The {new} COHERENCE proposed annotation ap- proach is also language and domain-independent {(although it has been applied nevertheless it is currently customized} COHERENCE for Brazilian Portuguese {)} FLUENCY . Table 1: An iteratively revised ArXiv abstract snippet (2103.14972, version 2, 3, and 4) with our annotated EDIT-INTENTION in ITERATER. not known before (Sommers, 1980). Specifically, text revision involves …
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AbstractWriting 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 generalizes to a variety of domains, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and action-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.
Publication Details
- Paper ID:
- 39128
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan