Grammatical Error Correction
117 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. |
Libraries
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Latest papers
GEE! Grammar Error Explanation with Large Language Models
To address this gap, we propose the task of grammar error explanation, where a system needs to provide one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences.
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models.
TLM: Token-Level Masking for Transformers
Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers.
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
System Combination via Quality Estimation for Grammatical Error Correction
However, we found that existing GEC quality estimation models are not good enough in differentiating good corrections from bad ones, resulting in a low F0. 5 score when used for system combination.
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost.
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks
Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the pace of development of generative models.
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction
In this paper, we aim to clarify how data augmentation improves GEC models.
RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
In this paper, we introduce RobustGEC, a benchmark designed to evaluate the context robustness of GEC systems.
FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation
Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently.