Papers with grammatical error detection
LinggleWrite: a Coaching System for Essay Writing (2020.acl-demos)
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| Challenge: | Existing writing services that provide feedback on writing skills are not providing sufficient "coaching" information. |
| Approach: | They propose a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user’s essay. |
| Outcome: | The proposed system improves on public test sets and shows that both AES and GED models achieve state-of-the-art performance. |
Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)
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| 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 . |
Syntax-guided Contrastive Learning for Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Existing studies rely on additional syntax-driven attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. |
| Approach: | They propose a syntax-guided contrastive learning method which does not change the transformer architecture and does not alter the transformer structure. |
| Outcome: | The proposed method achieves consistent improvements in a variety of tasks including grammatical error detection, entity tasks, structural probing and GLUE. |
Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation (2026.eacl-long)
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| Challenge: | Existing corpora address individual linguistic aspects like spelling or diacritization, but rarely provide explanations of grammatical errors. Existing datasets and benchmarks that capture Arabic's grammatological complexity are scarce. |
| Approach: | They propose a benchmark for Arabic grammar that covers error detection, correction, and explanation. |
| Outcome: | The proposed model performs better on GPT-4o than on the best performing model (ALLaM-7B) despite fine tuning with synthetic data, the model perform better on Arabic grammar tasks. |
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors (2022.findings-acl)
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| Challenge: | a language model-based error detection method can learn errors with a small training sample. |
| Approach: | They propose a language model-based method for grammatical error detection with feedback comments. |
| Outcome: | The proposed method can learn errors with a little training data and improve recall faster than non-language models. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |
Generation of a Spanish Artificial Collocation Error Corpus (L18-1)
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| Challenge: | collocations are combinations of two elements where one (the base) is freely chosen, despite the limitations of the other (collocate) current tools for collocation error detection and correction focus on collocation validation and identification of miscollocations . |
| Approach: | They propose an algorithm for automatic generation of an artificial collocation error corpus of american English learners of Spanish that includes 17 different types of collocation errors. |
| Outcome: | The proposed algorithm can detect and classify collocation errors in learners' writings . collocation error detection and correction has not received the attention it deserves . |
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)
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| Challenge: | Existing GEC models produce spurious corrections or fail to detect lots of errors. |
| Approach: | They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph . |
| Outcome: | The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets. |
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models (2022.lrec-1)
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Vésteinn Snæbjarnarson, Haukur Barri Símonarson, Pétur Orri Ragnarsson, Svanhvít Lilja Ingólfsdóttir, Haukur Jónsson, Vilhjalmur Thorsteinsson, Hafsteinn Einarsson
| Challenge: | Pre-trained neural language models have shown impressive results when adapted for a variety of classification and text generation tasks. |
| Approach: | They propose to use Icelandic's Icelandic Common Crawl Corpus to train language models that achieve state-of-the-art performance in downstream tasks. |
| Outcome: | The proposed models achieve state-of-the-art in a variety of downstream tasks including part-of speech tagging, named entity recognition and constituency parsing. |
The Tembusu Treebank: An English Learner Treebank (2022.lrec-1)
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| Challenge: | a new treebank is created to help diagnose ungrammatical sentences using mal-rules . the Tembusu Learner Treebank is an open treebank created from the corpus of Learner English . |
| Approach: | They propose to use the Tembusu Learner Treebank to train a new parse-ranking model for the English Resource Grammar . the model incorporates mal-rules in the annotation of ungrammatical sentences . |
| Outcome: | The Tembusu Learner Treebank is an open treebank created from the NTU Corpus of Learner English . the treebank is unique for incorporating mal-rules in the annotation of ungrammatical sentences . |
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (D18-1)
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| Challenge: | grammatical error correction is a labor-intensive task that requires large amounts of training data. |
| Approach: | They propose to use a human-annotated corpus of human-generated grammatical errors to generate a synthetic model. |
| Outcome: | The proposed method outperforms the current state of the art in grammatical error correction . human annotators achieve 39.39 F1 scores, suggesting the model generates mostly human-like instances . |
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator (2025.findings-acl)
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| Challenge: | Existing reference-free automatic grammatical error correction methods do not correlate with human evaluation. |
| Approach: | They propose a reference-free automatic grammatical error correction evaluation method with enhanced gramma-ed capabilities. |
| Outcome: | The proposed method achieves highest correlation with human evaluations on a meta-evaluation dataset. |