Grammatical Error Correction
114 papers with code • 11 benchmarks • 14 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.|
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Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task.
We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network.
Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits.
Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy.
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning
The resulting parallel corpora are subsequently used to pre-train Transformer models.
In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.
Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.
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.