GRAMMATICAL ERROR CORRECTION

MiSS: An Assistant for Multi-Style Simultaneous Translation (2021.emnlp-demo)

Challenge:	Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer.
Approach:	They propose to use text-editing models to predict edit operations applied to the source sequence and to generate outputs word-by-word from scratch.
Outcome:	This paper provides an overview of the text-edit based models and their current state-of-the-art approaches.

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Zuchao Li, Kevin Parnow, Masao Utiyama, Eiichiro Sumita, Hai Zhao

ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction (2022.naacl-main)

Challenge:	MiSS is a multi-style simultaneous translation assistant . it has five key features: high translation accuracy, simultaneous translation, flexibility, and measurable translation quality.
Approach:	They propose an assistant system for multi-style simultaneous translation that provides a complete translation experience for machine translation users.
Outcome:	The proposed system improves translation efficiency and performance by combining machine translation, grammatical error correction, and interactive edits.

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Xun Yuan, Derek Pham, Sam Davidson, Zhou Yu

Natural Language Interfaces

Challenge:	Currently available grammatical error correction datasets focus on written essays . a novel dataset is presented to improve the accuracy of existing educational chatbots .
Approach:	They propose a novel grammatical error correction dataset using essays and other long-form text written by language learners.
Outcome:	The proposed dataset improves the performance of a conversational chatbot in a human-machine conversational setting.

Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency (2020.acl-srw)

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Yujin Takahashi, Satoru Katsumata, Mamoru Komachi

Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models (P18-3)

Challenge:	Recent studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data.
Approach:	They propose to extract sentences similar to those written by language learners and generate pseudo errors by considering error types that learners often make.
Outcome:	The proposed model significantly improves the performance of the Russian GEC task compared with other models using pseudo data.

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Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi, Mamoru Komachi

Cool English: a Grammatical Error Correction System Based on Large Learner Corpora (C18-2)

Challenge:	Encoder-decoder models employ words that are frequently used in the training corpus but may still include noisy words.
Approach:	They propose a method for selecting more suitable words for learning encoders by utilizing co-occurrence information.
Outcome:	The proposed method outperforms the baseline method in Japanese-to-English translation and grammatical error correction tasks with an F-measure of 1.48 points higher.

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Yu-Chun Lo, Jhih-Jie Chen, Chingyu Yang, Jason Chang

A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction (2020.findings-emnlp)

Challenge:	Existing systems that correct grammatical errors are lacking in second language learning due to limited vocabulary and inadequate command of grammar.
Approach:	They propose a grammatical error correction system that provides corrective feedback for essays using a sequence-to-sequence model.
Outcome:	The proposed system achieves competitive performance on a number of publicly available testsets.

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Masato Mita, Shun Kiyono, Masahiro Kaneko, Jun Suzuki, Kentaro Inui

Czech Grammar Error Correction with a Large and Diverse Corpus (2022.tacl-1)

Challenge:	Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets.
Approach:	They propose to denoise GEC datasets by leveraging prediction consistency of existing models.
Outcome:	The proposed method outperforms baseline methods on CoNLL-2014, JFLEG, and BEA-2019 benchmarks.

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Jakub Náplava, Milan Straka, Jana Straková, Alexandr Rosen

Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems (N19-2)

Challenge:	a large and diverse corpus of Czech grammar error correction corpora is available for other languages . despite efforts to mitigate the notorious shortage of national GEC-annotated corpors, the lack of adequate data is even more acute in languages other than English.
Approach:	They propose to annotate a large and diverse Czech corpus for grammar error correction . they compare several Czech GEC systems and meta-evaluate common GEC metrics against human judgments on data.
Outcome:	The proposed corpus is annotated for grammar error correction (GEC) in Czech.

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Ashwini Challa, Kartikeya Upasani, Anusha Balakrishnan, Rajen Subba

Information Retrieval

Challenge:	Existing datasets for grammatical error correction don’t capture the distribution of errors that data-driven generators are likely to make.
Approach:	They propose a framework that allows candidates to be filtered and ranked to select the best response.
Outcome:	The proposed framework can be scaled with relatively low effort and achieve high precision with reasonable recall on a weather domain dataset.

Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)

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Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou

TEASPN: Framework and Protocol for Integrated Writing Assistance Environments (D19-3)

Challenge:	Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available.
Approach:	They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities .
Outcome:	The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text.

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Masato Hagiwara, Takumi Ito, Tatsuki Kuribayashi, Jun Suzuki, Kentaro Inui

Grammatical Error Correction in Low-Resource Scenarios (D19-55)

Challenge:	TEASPN is an open-source protocol for integrated writing assistance environments . authors propose that developers and researchers can integrate the latest developments in natural language processing with low cost.
Approach:	They propose a protocol and framework for integrating writing aids with writing software.
Outcome:	The proposed protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost.

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Jakub Náplava, Milan Straka

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting (2021.emnlp-main)

Challenge:	Existing systems for grammatical error correction in English have been limited . however, there is limited progress on error correction of other languages .
Approach:	They propose a dataset on grammatical error correction for Czech and an annotated learner corpus for Russian and Czech.
Outcome:	The proposed model can reach new state-of-the-art on Czech, German and Russian datasets.

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Wangchunshu Zhou, Tao Ge, Canwen Xu, Ke Xu, Furu Wei

Text Generation

Challenge:	Existing text infilling objectives for pretrained language models require self-supervision by masking out tokens or spans in text.
Approach:	They propose to extend text infilling to a self-supervised sequence-to-sequence (Seq2Sequen) task.
Outcome:	The proposed task improves the model's performance on various natural language generation tasks.

Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkish (2026.eacl-long)

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Yakup Abrek Er, Ilker Kesen, Gözde Gül Şahin, Aykut Erdem

Natural Language Interfaces

Challenge:	Existing Turkish benchmarks lack task diversity or culturally relevant content . Cetvel combines a broad range of discriminative and generative tasks .
Approach:	They propose a benchmark to evaluate large language models in Turkish . Cetvel combines a broad range of discriminative and generative tasks . they find that Turkish-centric instruction-tuned models generally underperform .
Outcome:	The proposed benchmark covers 23 tasks grouped into seven categories . it shows that Turkish-centric instruction-tuned models underperform relative to multilingual or general-purpose models despite being tailored for the language.

gec-metrics: A Unified Library for Grammatical Error Correction Evaluation (2025.acl-demo)

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Takumi Goto, Yusuke Sakai, Taro Watanabe

Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)

Challenge:	a library for using and developing grammatical error correction (GEC) evaluation metrics is released under the MIT license .
Approach:	They propose a library for using and developing grammatical error correction (GEC) evaluation metrics through a unified interface.
Outcome:	The proposed method is based on a unified evaluation framework with a strong focus on API usage and extensible.

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Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, Kenneth Heafield

From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems.
Approach:	They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings.
Outcome:	The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set.

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Xiuyu Wu, Yunfang Wu

Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)

Challenge:	Chinese Grammatical Error Correction (CGEC) aims to generate correct sentences from erroneous sequences.
Approach:	They propose a zero-shot approach for spelling error correction that is simple but effective . they propose auxiliary task to predict POS sequence of target sentence .
Outcome:	The proposed framework achieves 42.11 F-0.5 on the English GEC dataset outperforms the previous state-of-the-art by a wide margin of 1.30 points.

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Keqin Peng, Liang Ding, Qihuang Zhong, Yuanxin Ouyang, Wenge Rong, Zhang Xiong, Dacheng Tao

Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model (2020.aacl-main)

Challenge:	Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal.
Approach:	They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors.
Outcome:	Empirically, the proposed method yields significant improvements on three translation tasks.

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Satoru Katsumata, Mamoru Komachi

Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)

Challenge:	Strong pretraining approaches for grammatical error correction require extensive use of a pseudo-parallel corpus.
Approach:	They propose to use bidirectional and auto-regressive transformers as a generic pretrained encoder-decoder model for grammatical error correction (GEC) they find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC.
Outcome:	The proposed model achieves comparable results to the current strong results in English GEC.

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Anisia Katinskaia, Maria Lebedeva, Jue Hou, Roman Yangarber

Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human? (2025.acl-short)

Challenge:	Revita Learner Corpus is a semi-automatically annotated learner corpus for Russian . it is used for research in second language acquisition and foreign language teaching .
Approach:	They propose a semi-automatically annotated learner corpus for Russian that detects errors automatically and annotates errors by type.
Outcome:	The proposed corpus detects errors automatically and is annotated by type . the data is made public and the process is much cheaper and faster .

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Takumi Goto, Yusuke Sakai, Taro Watanabe

Fluency Boost Learning and Inference for Neural Grammatical Error Correction (P18-1)

Challenge:	Existing automatic evaluation metrics are based on procedures that diverge from human evaluation.
Approach:	They propose to aggregate automatic evaluation metrics to bridge this gap . they propose to use edit-based metrics, -gram based metrics and sentence-level metrics to find the best ranking system.
Outcome:	The proposed method outperforms existing metrics on the SEEDA benchmark and improves edit-based metrics, -gram based metrics and sentence-level metrics.

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Tao Ge, Furu Wei, Ming Zhou

Text Editing as Imitation Game (2022.findings-emnlp)

Challenge:	Seq2seq models for grammatical error correction (GEC) have two limitations: (1) a seq2q model may not be well generalized with only limited error-corrected data; (2) a model may fail to completely correct a sentence with multiple errors through normal seq1sequeq inference.
Approach:	They propose a fluency boost learning and inference mechanism to improve the performance of seq2seq models for grammatical error correction (GEC) by generating fluency-boost sentence pairs during training.
Outcome:	Experiments show that the proposed model improves on both CoNLL-2014 and JFLEG benchmark datasets.

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Ning Shi, Bin Tang, Bo Yuan, Longtao Huang, Yewen Pu, Jie Fu, Zhouhan Lin

Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough? (N19-1)

Challenge:	Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification.
Approach:	They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens.
Outcome:	The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness.

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Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, Kentaro Inui

Pseudo-Bidirectional Decoding for Local Sequence Transduction (2020.findings-emnlp)

Challenge:	Existing studies have evaluated grammatical error correction models on a single corpus, but the evaluation is incomplete because the task difficulty varies depending on the corpus and conditions such as proficiency levels of the writers and essay topics.
Approach:	They evaluate the performance of several GEC models against various learner corpora and compare their rankings against the corpus.
Outcome:	The evaluation of several models against learner corpora shows that the models’ rankings vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models.

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Wangchunshu Zhou, Tao Ge, Ke Xu

Frustratingly Easy System Combination for Grammatical Error Correction (2022.naacl-main)

Challenge:	Local sequence transduction tasks involve massive overlapping between source and target sequences . experimental results show that Pseudo-Bidirectional Decoding improves performance of standard seq2seq models.
Approach:	They propose a simple but versatile approach for local sequence transduction tasks . they propose to copy source tokens to decoder as pseudo future context .
Outcome:	The proposed approach improves the performance of standard seq2seq models on LST tasks.

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Muhammad Reza Qorib, Seung-Hoon Na, Hwee Tou Ng

Mitigating Exposure Bias in Grammatical Error Correction with Data Augmentation and Reweighting (2023.eacl-main)

Challenge:	Using a simple logistic regression algorithm, we combine GEC models for binary classification.
Approach:	They propose a logistic regression algorithm that can combine GEC models with binary classification.
Outcome:	The proposed method outperforms the state-of-the-art by 4.2 points on the CoNLL-2014 and 7.2 points on BEA-2019 test sets.

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Hannan Cao, Wenmian Yang, Hwee Tou Ng

Low-Resource NLP

Challenge:	Existing approaches to grammatical error correction (GEC) use sequence-to-sequence models, but there is an exposure bias problem.
Approach:	They propose a data manipulation approach to overcome the exposure bias problem in seq2seq GEC . they propose augmentation methods to mimic decoder input and reweighting methods to automatically balance the importance of each kind of augmented samples.
Outcome:	The proposed method improves on benchmark GEC datasets.

Grammatical Error Correction through Round-Trip Machine Translation (2023.findings-eacl)

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Yova Kementchedjhieva, Anders Søgaard

Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction (2024.findings-acl)

Challenge:	A decade ago the idea of using round-trip MT to guide grammatical error correction was not feasible due to the low quality of MT systems of the day.
Approach:	They propose to use round-trip machine translation to guide grammatical error correction to preserve meaning while mapping its surface form from one language into another.
Outcome:	The proposed system is re-examined across five languages and models of various sizes and yields consistent improvements.

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Yumeng Liu, Zhenghua Li, HaoChen Jiang, Bo Zhang, Chen Li, Ji Zhang

Unsupervised Grammatical Error Correction Rivaling Supervised Methods (2023.emnlp-main)

Challenge:	a high proportion of Chinese training data is multi-referenced for the grammatical error correction task . however, there are many ways to correct an erroneous input sentence . a systematic study on multi-referencing training data has been proposed .
Approach:	They propose two new approaches and a simple two-stage training strategy to better utilize multi-reference training data.
Outcome:	The proposed methods show that Chinese training data contain multiple references.

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Hannan Cao, Liping Yuan, Yuchen Zhang, Hwee Tou Ng

Low-Resource NLP

Challenge:	Current state-of-the-art grammatical error correction systems rely on labeled data . current systems require manual correction and require a large quantity of labeles .
Approach:	They propose an unsupervised method to build a grammatical error correction system using a fixer and a critic.
Outcome:	The proposed system outperforms previous unsupervised systems on English and Chinese GEC.

Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation (2020.coling-main)

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Takumi Gotou, Ryo Nagata, Masato Mita, Kazuaki Hanawa

Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction (2020.coling-main)

Challenge:	a paper aims to improve performance measures for grammatical error correction . conventional measures treat all errors equally, but some are easier to correct .
Approach:	They propose a way to determine the difficulty of error correction and to motivate researchers . paper examines performance measures for grammatical error correction using a scorer and weighting algorithm .
Outcome:	The proposed measures agree with our intuition of correction difficulty . the results show that the measures are more complex than conventional measures .

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Kengo Hotate, Masahiro Kaneko, Mamoru Komachi

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction (2020.coling-main)

Challenge:	Existing methods of acquiring diverse outputs focus on revising all tokens of a sentence.
Approach:	They propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap.
Outcome:	The proposed method generates more diverse corrections without losing accuracy in the local sequence transduction task.

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Charles Hinson, Hen-Hsen Huang, Hsin-Hsi Chen

Text Generation

Challenge:	Recent work in the field of grammatical error correction (GEC) rely on neural machine translation-based models.
Approach:	They propose a heterogeneous approach to Chinese grammatical error correction using NMT-based models, sequence editing models, and a spell checker.
Outcome:	The proposed model achieves state-of-the-art performance without data augmentation or changes in architecture . it adapts the ERRANT scorer to be able to score Chinese sentences .

Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models (2021.findings-emnlp)

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Gustavo Zomer, Ana Frankenberg-Garcia

TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)

Challenge:	Existing research on tools for improving writing focuses mostly on Grammatical Error Corrrection (GEC) but it does not adequately address fluency and complex linguistic issues.
Approach:	They propose a method for training a writing improvement model adapted to the writer’s first language (L1) without using annotated training data and use parallel corpora of reference translation aligned with machine translation.
Outcome:	The proposed model outperforms existing methods with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English.

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Tao Fang, Xuebo Liu, Derek F. Wong, Runzhe Zhan, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang

Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples (2020.emnlp-main)

Challenge:	Experimental results show that data augmentation improves accuracy over strong baselines.
Approach:	They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts .
Outcome:	The proposed method improves correction accuracy over strong baselines on four GEC benchmarks.

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Lihao Wang, Xiaoqing Zheng

Robustness in NLP

Challenge:	Existing methods for grammatical error correction are data-hungry and it is hard to train a seq2seq model with good performance without suf-Clean.
Approach:	They propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying weak spots of a model and to enhance the model by gradually adding adversarials to the training set.
Outcome:	The proposed method improves generalization and robustness of GEC models by adding adversarial examples to the training set.

Edit-Wise Preference Optimization for Grammatical Error Correction (2025.coling-main)

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Jiehao Liang, Haihui Yang, Shiping Gao, Xiaojun Quan

A Reassessment of Reference-Based Grammatical Error Correction Metrics (C18-1)

Challenge:	Large language models (LLMs) have been successful in grammatical error correction (GEC) but their strengths have yet to be fully demonstrated in GEC .
Approach:	They propose a method to optimize grammatical errors by assigning higher reward weights to edit tokens during preference optimization.
Outcome:	The proposed method outperforms baselines on English and Chinese datasets and achieves state-of-the-art performance.

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Shamil Chollampatt, Hwee Tou Ng

Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric (2021.emnlp-main)

Challenge:	Existing studies on the correlation of GEC metrics with human judgments were inconclusive . a recent study found that GLEU produces counter-intuitive scores in common test examples .
Approach:	They propose to use GLEU to evaluate grammatical error correction (GEC) systems . they also use statistical significance tests to assess their agreement with human judgments .
Outcome:	The proposed metrics show no significant advantage over MaxMatch (GLEU) the results contradict previous studies that claim GLEU superior .

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Md Asadul Islam, Enrico Magnani

Grammatical Error Correction: Are We There Yet? (2022.coling-1)

Challenge:	Existing evaluations of grammatical error correction systems use reference-based metrics, but they are limited because of multiple correct outputs.
Approach:	They propose a system that uses commonly available tools to evaluate grammatical error correction (GEC) systems.
Outcome:	The proposed system solves the issues related to the use of a reference and does not need another annotated dataset for fine-tuning.

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Muhammad Reza Qorib, Hwee Tou Ng

Unified Automated Essay Scoring and Grammatical Error Correction (2025.findings-naacl)

Challenge:	grammatical error correction (GEC) systems outperform humans on the CoNLL-2014 test set, but there are still classes of errors that they fail to correct.
Approach:	They found that state-of-the-art GEC systems outperform humans by a wide margin on the CoNLL-2014 test set . however, they found that there are still classes of errors that they fail to correct .
Outcome:	The F0.5 evaluation metric outperforms the CoNLL-2014 test set, but there are still classes of errors that they fail to correct.

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SeungWoo Song, Junghun Yuk, ChangSu Choi, HanGyeol Yoo, HyeonSeok Lim, KyungTae Lim, Jungyeul Park

CxGGEC: Construction-Guided Grammatical Error Correction (2025.acl-long)

Challenge:	a new study explores the integration of automated writing evaluation and grammatical error correction through multitask learning.
Approach:	They propose a system that integrates automated writing evaluation and grammatical error correction through multitask learning by leveraging a shared learning framework.
Outcome:	The proposed system outperforms models trained on AWE and GEC, the authors show . their study demonstrates that the proposed system improves writing assessment accuracy and accuracy .

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Yayu Cao, Tianxiang Wang, Lvxiaowei Xu, Zhenyao Wang, Ming Cai

New Dataset and Strong Baselines for the Grammatical Error Correction of Russian (2021.findings-acl)

Challenge:	Current GEC methods rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language.
Approach:	They propose to use construction grammar to capture underlying language patterns and guide corrections by decoding construction tokens into their original forms and correcting erroneous tokens.
Outcome:	The proposed model captures underlying language patterns and corrects erroneous construction tokens on English and Chinese benchmarks.

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Viet Anh Trinh, Alla Rozovskaya

Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020.acl-main)

Challenge:	a new resource is created to evaluate grammatical error correction models in English . a subset of the dataset is annotated in Russian, which is hard to come by and expensive to annotate .
Approach:	They develop an annotated learner corpus of Russian extracted from the Lang-8 website.
Outcome:	The proposed dataset is compared against two state-of-the-art grammatical error correction models . the results show that the created corpus is more diverse than the existing one .

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Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui

Do Grammatical Error Correction Models Realize Grammatical Generalization? (2021.findings-acl)

Challenge:	Existing methods for incorporating a masked language model into an EncDec model have potential drawbacks when applied to GEC.
Approach:	They propose to incorporate a pre-trained masked language model (MLM) into an encoder-decoder model for grammatical error correction.
Outcome:	The proposed method achieves state-of-the-art on BEA-2019 and CoNLL-2014 benchmarks.

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Masato Mita, Hitomi Yanaka

Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)

Challenge:	Existing models for grammatical error correction use pseudo data, but they are inconvenient for realworld deployment due to large amounts of training data.
Approach:	They propose a method to evaluate whether GEC models can generalize to unseen errors by using synthetic and real GEC datasets with controlled vocabularies.
Outcome:	The proposed model fails to realize grammatical generalization even in simple settings with limited vocabulary and syntax, suggesting it lacks the generalization ability required to correct errors from provided training examples.

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Liang Wang, Wei Zhao, Ruoyu Jia, Sujian Li, Jingming Liu

Cross-lingual Transfer Learning for Grammatical Error Correction (2020.coling-main)

Challenge:	PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques.
Approach:	They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text.
Outcome:	The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence.

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Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, Mamoru Komachi

Seq2Edits: Sequence Transduction Using Span-level Edit Operations (2020.emnlp-main)

Challenge:	Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient.
Approach:	They investigate cross-lingual transfer learning in grammatical error correction tasks . similarities between these languages is a key factor for successfully transferring grammatikal knowledge .
Outcome:	The proposed methods improve accuracy of grammatical error correction tasks in English and Russian, but lack the resources to train models in these languages.

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Felix Stahlberg, Shankar Kumar

Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (2021.findings-emnlp)

Challenge:	Seq2Edits is an open-vocabulary approach to sequence editing for natural language processing tasks with a high degree of overlap between input and output texts.
Approach:	They propose an open-vocabulary approach to sequence editing for NLP tasks with a high degree of overlap between input and output texts.
Outcome:	The proposed approach speeds up inference by up to 5.2x compared to full sequence models . it improves explainability by associating each edit operation with a human-readable tag.

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Hannan Cao, Wenmian Yang, Hwee Tou Ng

Semantic Text Processing

Challenge:	grammatical error correction (GEC) is a text generation task . performance on low error density domains where texts written by native speakers can be improved.
Approach:	They propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate.
Outcome:	The proposed approach significantly improves the performance of GEC models in low error density domains.

Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction (2023.findings-acl)

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Hejing Cao, Dongyan Zhao

Semantic Text Processing

Challenge:	Popular GEC models use large-scale synthetic corpora or use a large number of human-designed rules.
Approach:	They propose a model that incorporates denoised AMR as additional knowledge to get AMRs more reliable.
Outcome:	The proposed model reduces training time by 32% while inference time is comparable.

Revisiting Grammatical Error Correction Evaluation and Beyond (2022.emnlp-main)

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Peiyuan Gong, Xuebo Liu, Heyan Huang, Min Zhang

Position Offset Label Prediction for Grammatical Error Correction (2022.coling-1)

Challenge:	Pretraining-based (PT) evaluation metrics are not effective for training grammatical error correction systems.
Approach:	They propose a pretraining-based GEC evaluation metric which only uses PT-based metrics to score the corrected parts of the system.
Outcome:	The proposed evaluation metric outperforms existing methods on a CoNLL14 evaluation task.

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Xiuyu Wu, Jingsong Yu, Xu Sun, Yunfang Wu

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

Challenge:	Experimental results show that our proposed POL-Pc framework improves baseline models and yields consistent performance gain over various data augmentation methods.
Approach:	They propose a position offset label prediction subtask to integrate correction editing operations into a unified framework.
Outcome:	The proposed model outperforms baseline models on Chinese, English and Japanese datasets by a wide margin.

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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Multi-Perspective Document Revision (2022.coling-1)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Mana Ihori, Hiroshi Sato, Tomohiro Tanaka, Ryo Masumura

Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)

Challenge:	a novel document revision task that revises multiple perspectives is proposed . grammatical error correction tasks have been studied in the natural language processing field .
Approach:	They propose a Japanese multi-perspective document revision task that revises multiple perspectives to improve the readability and clarity of a document.
Outcome:	The proposed model can be used to improve the readability and clarity of a document.

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Andrea Sottana, Bin Liang, Kai Zou, Zheng Yuan

Multi-pass Decoding for Grammatical Error Correction (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement.
Approach:	They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction.
Outcome:	The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics.

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Xiaoying Wang, Lingling Mu, Jingyi Zhang, Hongfei Xu

Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency (2024.lrec-main)

Challenge:	Seq2edit models decode only once without aware of subsequent tokens.
Approach:	They propose to iteratively refine the correction results of seq2seq models via Multi-Pass Decoding (MPD) to improve performance, but MPD increases inference costs . they propose to merge the source input and previous round correction result into one sequence.
Outcome:	Experiments on the CoNLL-14 and BEA-19 test set show that the proposed approach improves over baselines.

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Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park

Low-Resource NLP

Challenge:	generative LLMs have been known for overcorrection where results obtain higher recall measures than precision measures.
Approach:	They propose to use generative LLMs to prompt grammatical error correction using a model based on language proficiency to examine the interaction between LLM's performance and L2 language proficiency.
Outcome:	The proposed model improves on zero-shot and few-shot prompting and fine-tuning models for grammatical error correction for learners of English as a foreign language based on the different proficiency levels.

SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction (2020.coling-main)

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Ryoma Yoshimura, Masahiro Kaneko, Tomoyuki Kajiwara, Mamoru Komachi

Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models (2022.acl-long)

Challenge:	Existing reference-less metrics are not optimized for manual evaluations of system outputs because no dataset exists for manual analysis.
Approach:	They propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction.
Outcome:	The proposed metric improves correlation with manual evaluation in system- and sentence-level meta-evaluation.

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Felix Stahlberg, Ilia Kulikov, Shankar Kumar

Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)

Challenge:	In many natural language processing tasks the same input can have multiple possible outputs.
Approach:	They propose a novel exact n-best search algorithm for neural sequence models to measure sentence-level uncertainty by computing the degree of overlap between references from two different NLP tasks.
Outcome:	The proposed algorithm overly spreads the probability mass for uncertain tasks and sentences.

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Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi

Reducing Sequence Length by Predicting Edit Spans with Large Language Models (2023.emnlp-main)

Challenge:	Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English.
Approach:	They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
Outcome:	The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation.

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Masahiro Kaneko, Naoaki Okazaki

ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction (2022.lrec-1)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention.
Approach:	They propose to predict edit spans for local sequence transduction tasks by predicting edit span with a position of the source text and corrected tokens.
Outcome:	The proposed method reduces the length of the target sequence and the computational cost for inference by as small as 21%.

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Yujin Takahashi, Masahiro Kaneko, Masato Mita, Mamoru Komachi

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks (2022.emnlp-main)

Challenge:	Prior work has shown that QE models of grammatical error correction are biased toward data by learners with relatively high proficiency levels.
Approach:	They investigated whether learners' proficiency affects supervised quality estimation models of grammatical error correction (GEC) . they created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC.
Outcome:	The proposed model is based on multiple proficiency levels and can be performed in real-world scenarios.

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Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)

Challenge:	Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness.
Approach:	They propose to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans with their corresponding edit intents.
Outcome:	The proposed system outperforms baselines on other text revision tasks and human evaluations.

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Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard

Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)

Challenge:	CWEB is a new benchmark for grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays .
Approach:	They propose to broaden the target domain of grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays .
Outcome:	The proposed model can't rely on a strong internal language model in low error density domains.

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Zheng Yuan, Shiva Taslimipoor, Christopher Davis, Christopher Bryant

To Err Is Human, but Llamas Can Learn It Too (2024.findings-emnlp)

Challenge:	a multi-class grammatical error detection system can be used to improve grammamatical errors correction (GEC) for English.
Approach:	They develop a multi-class grammatical error detection system based on pre-trained ELECTRA and extend it to multi-Class detection using different error type tagsets.
Outcome:	The proposed system outperforms previous systems on the BEA-test benchmark.

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Agnes Luhtaru, Taido Purason, Martin Vainikko, Maksym Del, Mark Fishel

System Combination via Quality Estimation for Grammatical Error Correction (2023.emnlp-main)

Challenge:	Specifically, we fine-tune Llama 2 LMs for error generation and find that this approach yields synthetic errors akin to human errors.
Approach:	They propose to fine-tune Llama 2 LMs for error generation and train GEC Llma models using these artificial errors.
Outcome:	The proposed approach outperforms state-of-the-art models with gains ranging between 0.8 and 6 F0.5 points across all languages tested.

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Muhammad Reza Qorib, Hwee Tou Ng

Improved grammatical error correction by ranking elementary edits (2022.emnlp-main)

Challenge:	Existing quality estimation models are not good enough to distinguish good corrections from bad ones, resulting in low F0.5 scores when used for system combination.
Approach:	They propose a new quality estimation model that gives a better estimate of the quality of a corrected sentence.
Outcome:	The proposed model outperforms the state-of-the-art on the CoNLL-2014 and BEA-2019 test sets, and achieves the highest F0.5 scores published to date.

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Alexey Sorokin

GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors (2020.lrec-1)

Challenge:	a new study shows that grammatical error correction models are far from perfect for English . reranking allows for a better classification of edits, but it can be difficult for other languages .
Approach:	They propose a two-stage reranking method for grammatical error correction using a model as edit generator and a sequence labeling model as second step.
Outcome:	The proposed method surpasses existing methods on BEA 2019 English dataset by 2-3%.

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Masato Hagiwara, Masato Mita

Information Retrieval

Challenge:	Lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction.
Approach:	They propose to make GitHub Typo Corpus a multilingual dataset of misspellings and grammatical errors available for use in NLP.
Outcome:	The proposed dataset contains more than 350k edits and 65M characters in more than 15 languages.

Developing NLP Tools with a New Corpus of Learner Spanish (2020.lrec-1)

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Sam Davidson, Aaron Yamada, Paloma Fernandez Mira, Agustina Carando, Claudia H. Sanchez Gutierrez, Kenji Sagae

Efficient and Interpretable Grammatical Error Correction with Mixture of Experts (2024.findings-emnlp)

Challenge:	Currently, there is little research on the development of effective NLP tools for the L2 classroom.
Approach:	They propose to use an annotated corpus of Spanish learner text to analyze developmental patterns and to develop a grammatical error correction system for Spanish learners.
Outcome:	The proposed system is based on annotated learner corpus of Spanish learners and includes error annotations and corrected text.

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Muhammad Qorib, Alham Aji, Hwee Tou Ng

Responsible & Trustworthy NLP

Challenge:	Error type information has been widely used to improve the performance of grammatical error correction models.
Approach:	They propose a mixture-of-experts model for grammatical error correction that uses error type information to generate corrections and combine models.
Outcome:	The proposed model achieves the performance of T5-XL with three times fewer effective parameters and produces interpretable corrections by also identifying the error type during inference.

Low-Resource Grammatical Error Correction: Selective Data Augmentation with Round-Trip Machine Translation (2025.findings-acl)

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Frank Palma Gomez, Alla Rozovskaya

Reliability Crisis of Reference-free Metrics for Grammatical Error Correction (2025.findings-emnlp)

Challenge:	Existing methods for grammatical error correction require large amounts of parallel training data.
Approach:	They propose to generate synthetic data through round-trip machine translation by generating a set of character-level errors using a technique known as SeLex-RT.
Outcome:	The proposed technique produces errors similar to those observed with language learners, but lacks gold-labeled training data.

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Takumi Goto, Yusuke Sakai, Taro Watanabe

Robustness in NLP

Challenge:	Reference-free evaluation metrics for grammatical error correction have high correlation with human judgments, but they are not designed to evaluate adversarial systems that aim to obtain unjustifiably high scores.
Approach:	They propose adversarial attack strategies for four reference-free metrics . they propose SOME, Scribendi, IMPARA, and LLM-based metrics based on these metrics a .
Outcome:	The proposed attacks outperform the current state-of-the-art for four reference-free metrics .

Universal Dependencies for Learner Russian (2024.lrec-main)

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Alla Rozovskaya

CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction (2026.acl-long)

Challenge:	a pilot study of Russian learner data with syntactic dependency relations is presented . a focus of recent work in the NLP community has been on grammar errors .
Approach:	They propose to annotate Russian learner data with syntactic dependency relations using a subset of sentences from two error-corrected Russian learners.
Outcome:	The proposed annotations are performed on a subset of Russian learner datasets.

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Shang Qin, Jingheng Ye, Yinghui Li, Hai-Tao Zheng, Qi Li, Jinxiao Shan, Zhixing Li, Hong-Gee Kim

Edit-Aware Reward Modeling for Chinese Grammatical Error Correction (2026.acl-long)

Challenge:	Existing CGEC benchmarks for multi-disciplinary writing are limited . continual learning (CL) is a promising solution to handle domain-specific linguistic variation and prevent catastrophic forgetting.
Approach:	They propose a Chinese Literature Continual Learning benchmark to evaluate adaptive CGEC across disciplines.
Outcome:	The proposed benchmark includes 10,000 human-annotated sentences spanning 10 disciplines, each exhibiting distinct linguistic styles and error patterns.

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Yilin Li, Xiaojun Wan