Grammatical Error Correction
121 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
Latest papers with no code
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction
Experiments on the BEA-2019 shared task and the CoNLL-2014 shared task have shown that AMR-GEC performs comparably to a set of strong baselines with a large number of synthetic data.
A Language Model for Grammatical Error Correction in L2 Russian
Grammatical error correction is one of the fundamental tasks in Natural Language Processing.
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese
We investigate the effectiveness of GPT-3. 5 and GPT-4, two large language models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese and compare their performance against Microsoft Word and Google Docs.
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction
Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively.
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks.
Reducing Sequence Length by Predicting Edit Operations with Large Language Models
Experiments show that the proposed method achieves comparable performance to the baseline in four tasks, paraphrasing, formality style transfer, GEC, and text simplification, despite reducing the length of the target text by as small as 21%.
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.
A BERT-based Unsupervised Grammatical Error Correction Framework
Grammatical error correction (GEC) is a challenging task of natural language processing techniques.
Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction
GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks.
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark
ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions.
