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    <titleInfo>
        <title>System Combination via Quality Estimation for Grammatical Error Correction</title>
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        <namePart type="given">Muhammad</namePart>
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    <abstract>Quality estimation models have been developed to assess the corrections made by grammatical error correction (GEC) models when the reference or gold-standard corrections are not available. An ideal quality estimator can be utilized to combine the outputs of multiple GEC systems by choosing the best subset of edits from the union of all edits proposed by the GEC base systems. 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. In this paper, we propose GRECO, a new state-of-the-art quality estimation model that gives a better estimate of the quality of a corrected sentence, as indicated by having a higher correlation to the F0.5 score of a corrected sentence. It results in a combined GEC system with a higher F0.5 score. We also propose three methods for utilizing GEC quality estimation models for system combination with varying generality: model-agnostic, model-agnostic with voting bias, and model-dependent method. The combined GEC system outperforms the state of the art on the CoNLL-2014 test set and the BEA-2019 test set, achieving the highest F0.5 scores published to date.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.785</identifier>
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%0 Conference Proceedings
%T System Combination via Quality Estimation for Grammatical Error Correction
%A Qorib, Muhammad Reza
%A Ng, Hwee Tou
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F qorib-ng-2023-system
%X Quality estimation models have been developed to assess the corrections made by grammatical error correction (GEC) models when the reference or gold-standard corrections are not available. An ideal quality estimator can be utilized to combine the outputs of multiple GEC systems by choosing the best subset of edits from the union of all edits proposed by the GEC base systems. 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. In this paper, we propose GRECO, a new state-of-the-art quality estimation model that gives a better estimate of the quality of a corrected sentence, as indicated by having a higher correlation to the F0.5 score of a corrected sentence. It results in a combined GEC system with a higher F0.5 score. We also propose three methods for utilizing GEC quality estimation models for system combination with varying generality: model-agnostic, model-agnostic with voting bias, and model-dependent method. The combined GEC system outperforms the state of the art on the CoNLL-2014 test set and the BEA-2019 test set, achieving the highest F0.5 scores published to date.
%R 10.18653/v1/2023.emnlp-main.785
%U https://aclanthology.org/2023.emnlp-main.785
%U https://doi.org/10.18653/v1/2023.emnlp-main.785
%P 12746-12759

Markdown (Informal)

[System Combination via Quality Estimation for Grammatical Error Correction](https://aclanthology.org/2023.emnlp-main.785) (Qorib & Ng, EMNLP 2023)

• System Combination via Quality Estimation for Grammatical Error Correction (Qorib & Ng, EMNLP 2023)

ACL

• Muhammad Reza Qorib and Hwee Tou Ng. 2023. System Combination via Quality Estimation for Grammatical Error Correction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12746–12759, Singapore. Association for Computational Linguistics.