# Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

arXiv: https://arxiv.org/abs/2205.11484 · HTML: https://mlsanity.com/paper/2205.11484/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 94 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020)
- [Understanding Iterative Revision from Human-Written Text](https://mlsanity.com/paper/2203.03802/) (2022)
- [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019)
- [Transparent Human Evaluation for Image Captioning](https://mlsanity.com/paper/2111.08940/) (2021)
- [A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://mlsanity.com/paper/1801.08831/) (2018)
- [Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand](https://mlsanity.com/paper/2112.04139/) (2021)
- [GLEU Without Tuning](https://mlsanity.com/paper/1605.02592/) (2016)
- [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://mlsanity.com/paper/1807.01270/) (2018)
- [There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction](https://mlsanity.com/paper/1610.02124/) (2016)
- [Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses](https://mlsanity.com/paper/2010.07574/) (2020)
- [A Nested Attention Neural Hybrid Model for Grammatical Error Correction](https://mlsanity.com/paper/1707.02026/) (2017)
- [Universal Dependencies for Learner English](https://mlsanity.com/paper/1605.04278/) (2016)
- [Pairwise Neural Machine Translation Evaluation](https://mlsanity.com/paper/1912.03135/) (2019)
- [Reference-less Measure of Faithfulness for Grammatical Error Correction](https://mlsanity.com/paper/1804.03824/) (2018)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?](https://mlsanity.com/paper/1904.02927/) (2019)

## Built on by (most cited first, 20 of 20)

- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Aligning LLM Agents by Learning Latent Preference from User Edits](https://mlsanity.com/paper/2404.15269/) (2024)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [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)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions](https://mlsanity.com/paper/2403.00241/) (2024)
- [ScholaWrite: A Dataset of End-to-End Scholarly Writing Process](https://mlsanity.com/paper/2502.02904/) (2025)
- [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://mlsanity.com/paper/2503.00985/) (2025)
- [eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback](https://mlsanity.com/paper/2501.00715/) (2025)
- [Identifying Reliable Evaluation Metrics for Scientific Text Revision](https://mlsanity.com/paper/2506.04772/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction](https://mlsanity.com/paper/2510.00268/) (2025)
- [GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors](https://mlsanity.com/paper/2506.08188/) (2025)
- [The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors](https://mlsanity.com/paper/2603.00925/) (2026)
- [EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces](https://mlsanity.com/paper/2603.28515/) (2026)
- [Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward](https://mlsanity.com/paper/2601.19055/) (2026)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)
- [Incentives Of EdTech: A Systematic Review Of EduNLP Research](https://mlsanity.com/paper/2606.13691/) (2026)
- [Metag: A dataset to build agentic meta-reviewing capabilities](https://mlsanity.com/paper/2608.20488/) (2026)

## Abstract (arXiv)

Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges. To go beyond sentence-level automated grammatical error correction to NLP-based document-level revision assistant, there are two major obstacles: (1) there are few public corpora with document-level revisions being annotated by professional editors, and (2) it is not feasible to elicit all possible references and evaluate the quality of revision with such references because there are infinite possibilities of revision. This paper tackles these challenges. First, we introduce a new document-revision corpus, TETRA, where professional editors revised academic papers sampled from the ACL anthology which contain few trivial grammatical errors that enable us to focus more on document- and paragraph-level edits such as coherence and consistency. Second, we explore reference-less and interpretable methods for meta-evaluation that can detect quality improvements by document revision. We show the uniqueness of TETRA compared with existing document revision corpora and demonstrate that a fine-tuned pre-trained language model can discriminate the quality of documents after revision even when the difference is subtle. This promising result will encourage the community to further explore automated document revision models and metrics in future.