Grammatical Error Correction
118 papers with code • 11 benchmarks • 15 datasets
Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.
GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:
| Input (Erroneous) | Output (Corrected) |
|---|---|
| She see Tom is catched by policeman in park at last night. | She saw Tom caught by a policeman in the park last night. |
Benchmarks
These leaderboards are used to track progress in Grammatical Error Correction
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Libraries
Use these libraries to find Grammatical Error Correction models and implementationsDatasets
CoNLL
JFLEG
CoNLL-2014 Shared Task: Grammatical Error Correction
WI-LOCNESS
GitHub Typo Corpus
Most implemented papers
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
hillzhang1999/mucgec
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NAACL 2022
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
microsoft/unilm
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We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.
Mining Error Templates for Grammatical Error Correction
We have accumulated 1, 119 error templates for Chinese GEC based on this method.
Chinese grammatical error correction based on knowledge distillation
In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
xlxwalex/FCGEC
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Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently.
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.
