# WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse

arXiv: https://arxiv.org/abs/1808.09422 · HTML: https://mlsanity.com/paper/1808.09422/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 26 of 26)

- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://mlsanity.com/paper/2404.13948/) (2024)
- [Understanding Iterative Revision from Human-Written Text](https://mlsanity.com/paper/2203.03802/) (2022)
- [Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision](https://mlsanity.com/paper/2204.03685/) (2022)
- [Learning to Model Editing Processes](https://mlsanity.com/paper/2205.12374/) (2022)
- [arXivEdits: Understanding the Human Revision Process in Scientific Writing](https://mlsanity.com/paper/2210.15067/) (2022)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions](https://mlsanity.com/paper/2403.00241/) (2024)
- [Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision](https://mlsanity.com/paper/2406.00197/) (2024)
- [Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback](https://mlsanity.com/paper/2507.16007/) (2025)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)
- [Controlling Text Edition by Changing Answers of Specific Questions](https://mlsanity.com/paper/2105.11018/) (2021)
- [XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates](https://mlsanity.com/paper/2309.11063/) (2023)
- [Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS](https://mlsanity.com/paper/2306.05083/) (2023)
- [DIFFQG: Generating Questions to Summarize Factual Changes](https://mlsanity.com/paper/2303.00242/) (2023)
- [SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts](https://mlsanity.com/paper/2309.12102/) (2023)
- [Audience-specific Explanations for Machine Translation](https://mlsanity.com/paper/2309.12998/) (2023)
- [Misspellings in Natural Language Processing: A survey](https://mlsanity.com/paper/2501.16836/) (2025)
- [NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)](https://mlsanity.com/paper/2104.09647/) (2021)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Ruri: Japanese General Text Embeddings](https://mlsanity.com/paper/2409.07737/) (2024)
- [NewsEdits 2.0: Learning the Intentions Behind Updating News](https://mlsanity.com/paper/2411.18811/) (2024)
- [An Inclusive Notion of Text](https://mlsanity.com/paper/2211.05604/) (2022)
- [WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural Language Instruction](https://mlsanity.com/paper/2310.05009/) (2023)
- [RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romanian](https://mlsanity.com/paper/2604.19593/) (2026)

## Abstract (arXiv)

We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw, unstructured text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.