| Challenge: | We present a lightweight web application to serve grammatical error correction systems . ALLECS is designed to be accessible to as many users as possible . |
| Approach: | They propose a web application that can serve grammatical error correction systems . they propose to provide three state-of-the-art base GEC systems and two combine methods . |
| Outcome: | The proposed system can be easily used by the general public and is available for free on nus.edu.sg. |
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A Crash Course in Automatic Grammatical Error Correction (2020.coling-tutorials)
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| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
| Approach: | tutorial aims to introduce participants to the field of Grammatical Error Correction . aim is to examine the development of neural-based GEC systems . |
| Outcome: | the tutorial aims to introduce participants to the current state of the art in the field of Grammatical Error Correction (GEC) |
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)
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| 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. |
Grammatical Error Correction: Are We There Yet? (2022.coling-1)
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| 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. |
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction (2023.findings-eacl)
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| Challenge: | Current sequence-to-sequence and sequence-tagging approaches treat GEC as a machine-translation problem. |
| Approach: | They propose to introduce specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. |
| Outcome: | The proposed approach outperforms existing methods on the BEA benchmark. |
Cool English: a Grammatical Error Correction System Based on Large Learner Corpora (C18-2)
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| 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. |
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study (2025.acl-long)
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| Challenge: | Text editing is a wellstudied problem for grammatical error correction (GEC) but it is not the most efficient for morphologically rich languages like Arabic. |
| Approach: | They propose a text editing approach that derives edit tags directly from data, eliminating the need for language-specific edits. |
| Outcome: | The proposed approach achieves SOTA results on Arabic and performs on par with SOTA on two other languages. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |
gec-metrics: A Unified Library for Grammatical Error Correction Evaluation (2025.acl-demo)
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| 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. |
UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework (2025.coling-demos)
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| Challenge: | Existing tools for GEC have been developed to support research on grammatical errors, but there is no comprehensive evaluation on these models. |
| Approach: | They propose an open-source framework for Grammatical Error Correction that integrates 5 widely-used GEC models and compares their performance on 7 datasets in different languages. |
| Outcome: | The proposed framework compares 5 widely-used models on 7 datasets in different languages. |
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |