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        <title>From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction</title>
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    <name type="personal">
        <namePart type="given">Xiuyu</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2022</title>
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            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
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        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. We firstly propose a novel zero-shot approach for spelling error correction, which is simple but effective, obtaining a high precision to avoid error accumulation of the pipeline structure. To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence. Our proposed framework achieves a 42.11 F-0.5 score on CGEC dataset without using any synthetic data or data augmentation methods, which outperforms the previous state-of-the-art by a wide margin of 1.30 points. Moreover, our model produces meaningful POS representations that capture different POS words and convey reasonable POS transition rules.</abstract>
    <identifier type="citekey">wu-wu-2022-spelling</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-emnlp.63</identifier>
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        <date>2022-12</date>
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            <start>889</start>
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%0 Conference Proceedings
%T From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction
%A Wu, Xiuyu
%A Wu, Yunfang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wu-wu-2022-spelling
%X Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. We firstly propose a novel zero-shot approach for spelling error correction, which is simple but effective, obtaining a high precision to avoid error accumulation of the pipeline structure. To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence. Our proposed framework achieves a 42.11 F-0.5 score on CGEC dataset without using any synthetic data or data augmentation methods, which outperforms the previous state-of-the-art by a wide margin of 1.30 points. Moreover, our model produces meaningful POS representations that capture different POS words and convey reasonable POS transition rules.
%R 10.18653/v1/2022.findings-emnlp.63
%U https://aclanthology.org/2022.findings-emnlp.63
%U https://doi.org/10.18653/v1/2022.findings-emnlp.63
%P 889-902

Markdown (Informal)

[From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://aclanthology.org/2022.findings-emnlp.63) (Wu & Wu, Findings 2022)

• From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction (Wu & Wu, Findings 2022)

ACL

• Xiuyu Wu and Yunfang Wu. 2022. From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 889–902, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.