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--- |
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language: en |
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tags: |
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- greco |
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- grammar |
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- grammaticality |
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- gec |
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base_model: microsoft/deberta-v3-large |
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datasets: w&i+locness |
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model-index: |
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- name: GRECO |
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results: |
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- task: |
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type: grammatical-error-correction |
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name: Grammatical Error Correction |
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dataset: |
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type: conll-2014-shared-task-grammatical-error |
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name: CoNLL-2014 |
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split: test |
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metrics: |
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- type: f0.5 |
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value: 71.12 |
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name: F0.5 |
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source: |
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name: NLP-progress |
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url: https://nlpprogress.com/english/grammatical_error_correction.html |
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license: gpl-3.0 |
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--- |
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# GRECO: Gammaticality-scorer for re-ranking corrections |
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GRECO is a quality estimation model for grammatical error correction. The model is trained to detect which words are incorrect and whether a word or phrase needs to be inserted after certain words. You can then use the model to get the grammaticality score of a sentence. |
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Please check the [official repository](https://github.com/nusnlp/greco/tree/main) for more implementation details and updates. |
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The model was published in the following paper: |
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> System Combination via Quality Estimation for Grammatical Error Correction ([PDF](https://arxiv.org/abs/2310.14947) | [ACL Anthology](https://aclanthology.org/2023.emnlp-main.785/)) <br> |
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> [Muhammad Reza Qorib](https://mrqorib.github.io/) and [Hwee Tou Ng](https://www.comp.nus.edu.sg/~nght/) <br> |
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> The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
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## Citation |
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If you find it useful for your work, please cite the paper: |
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```latex |
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@inproceedings{qorib-ng-2023-system, |
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title = "System Combination via Quality Estimation for Grammatical Error Correction", |
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author = "Qorib, Muhammad Reza and |
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Ng, Hwee Tou", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.785", |
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doi = "10.18653/v1/2023.emnlp-main.785", |
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pages = "12746--12759", |
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} |
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``` |
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