# ArgRewrite V.2: an Annotated Argumentative Revisions Corpus

arXiv: https://arxiv.org/abs/2206.01677 · HTML: https://mlsanity.com/paper/2206.01677/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 76 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings](https://mlsanity.com/paper/1511.06388/) (2015)
- [A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication](https://mlsanity.com/paper/1405.1439/) (2014)
- [Annotation and Classification of Sentence-level Revision Improvement](https://mlsanity.com/paper/1909.05309/) (2019)
- [Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing](https://mlsanity.com/paper/2107.07018/) (2021)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [StRE: Self Attentive Edit Quality Prediction in Wikipedia](https://mlsanity.com/paper/1906.04678/) (2019)
- [EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets](https://mlsanity.com/paper/1802.09233/) (2018)
- [Predicting Specificity in Classroom Discussion](https://mlsanity.com/paper/1909.01462/) (2019)

## Built on by (most cited first, 9 of 9)

- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [arXivEdits: Understanding the Human Revision Process in Scientific Writing](https://mlsanity.com/paper/2210.15067/) (2022)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)
- [Predicting the Quality of Revisions in Argumentative Writing](https://mlsanity.com/paper/2306.00667/) (2023)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)
- [SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts](https://mlsanity.com/paper/2309.12102/) (2023)
- [Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks](https://mlsanity.com/paper/2406.15130/) (2024)
- [FOXGLOVE: Understanding Goal-Oriented and Anchored Writing Feedback from Experts and LLMs on Argumentative Essays](https://mlsanity.com/paper/2606.06271/) (2026)
- [MixDetect: Word-Level Localization and Quantification of AI Editing](https://mlsanity.com/paper/2609.32625/) (2026)

## Abstract (arXiv)

Analyzing how humans revise their writings is an interesting research question, not only from an educational perspective but also in terms of artificial intelligence. Better understanding of this process could facilitate many NLP applications, from intelligent tutoring systems to supportive and collaborative writing environments. Developing these applications, however, requires revision corpora, which are not widely available. In this work, we present ArgRewrite V.2, a corpus of annotated argumentative revisions, collected from two cycles of revisions to argumentative essays about self-driving cars. Annotations are provided at different levels of purpose granularity (coarse and fine) and scope (sentential and subsentential). In addition, the corpus includes the revision goal given to each writer, essay scores, annotation verification, pre- and post-study surveys collected from participants as meta-data. The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis. We demonstrate some potential applications of ArgRewrite V.2 in the development of automatic revision purpose predictors, as a training source and benchmark.