# Annotation and Classification of Sentence-level Revision Improvement

arXiv: https://arxiv.org/abs/1909.05309 · HTML: https://mlsanity.com/paper/1909.05309/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [ArgRewrite V.2: an Annotated Argumentative Revisions Corpus](https://mlsanity.com/paper/2206.01677/) (2022)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)
- [A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality](https://mlsanity.com/paper/2404.02529/) (2024)
- [Claim Optimization in Computational Argumentation](https://mlsanity.com/paper/2212.08913/) (2022)
- [Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction](https://mlsanity.com/paper/2510.00268/) (2025)
- [The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling](https://mlsanity.com/paper/2307.15508/) (2023)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)

## Abstract (arXiv)

Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.