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    <titleInfo>
        <title>Efficient and Interpretable Grammatical Error Correction with Mixture of Experts</title>
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        <namePart type="given">Muhammad</namePart>
        <namePart type="given">Reza</namePart>
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        <namePart type="given">Alham</namePart>
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    <name type="personal">
        <namePart type="given">Hwee</namePart>
        <namePart type="given">Tou</namePart>
        <namePart type="family">Ng</namePart>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that have complementary strengths in correcting different error types is very effective in producing better corrections. However, system combination incurs a high computational cost due to the need to run inference on the base systems before running the combination method itself. Therefore, it would be more efficient to have a single model with multiple sub-networks that specialize in correcting different error types. In this paper, we propose a mixture-of-experts model, MoECE, for grammatical error correction. Our model successfully achieves the performance of T5-XL with three times fewer effective parameters. Additionally, our model produces interpretable corrections by also identifying the error type during inference.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-emnlp.997</identifier>
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%0 Conference Proceedings
%T Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
%A Qorib, Muhammad Reza
%A Aji, Alham Fikri
%A Ng, Hwee Tou
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F qorib-etal-2024-efficient
%X Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that have complementary strengths in correcting different error types is very effective in producing better corrections. However, system combination incurs a high computational cost due to the need to run inference on the base systems before running the combination method itself. Therefore, it would be more efficient to have a single model with multiple sub-networks that specialize in correcting different error types. In this paper, we propose a mixture-of-experts model, MoECE, for grammatical error correction. Our model successfully achieves the performance of T5-XL with three times fewer effective parameters. Additionally, our model produces interpretable corrections by also identifying the error type during inference.
%R 10.18653/v1/2024.findings-emnlp.997
%U https://aclanthology.org/2024.findings-emnlp.997/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.997
%P 17127-17138

Markdown (Informal)

[Efficient and Interpretable Grammatical Error Correction with Mixture of Experts](https://aclanthology.org/2024.findings-emnlp.997/) (Qorib et al., Findings 2024)

• Efficient and Interpretable Grammatical Error Correction with Mixture of Experts (Qorib et al., Findings 2024)

ACL

• Muhammad Reza Qorib, Alham Fikri Aji, and Hwee Tou Ng. 2024. Efficient and Interpretable Grammatical Error Correction with Mixture of Experts. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 17127–17138, Miami, Florida, USA. Association for Computational Linguistics.