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    <titleInfo>
        <title>Improved grammatical error correction by ranking elementary edits</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
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        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>We offer a two-stage reranking method for grammatical error correction: the first model serves as edit generator, while the second classifies the proposed edits as correct or false. We show how to use both encoder-decoder and sequence labeling models for the first step of our pipeline. We achieve state-of-the-art quality on BEA 2019 English dataset even using weak BERT-GEC edit generator. Combining our roberta-base scorer with state-of-the-art GECToR edit generator, we surpass GECToR by 2-3%. With a larger model we establish a new SOTA on BEA development and test sets. Our model also sets a new SOTA on Russian, despite using smaller models and less data than the previous approaches.</abstract>
    <identifier type="citekey">sorokin-2022-improved</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.785</identifier>
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        <url>https://aclanthology.org/2022.emnlp-main.785</url>
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        <date>2022-12</date>
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            <start>11416</start>
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%0 Conference Proceedings
%T Improved grammatical error correction by ranking elementary edits
%A Sorokin, Alexey
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sorokin-2022-improved
%X We offer a two-stage reranking method for grammatical error correction: the first model serves as edit generator, while the second classifies the proposed edits as correct or false. We show how to use both encoder-decoder and sequence labeling models for the first step of our pipeline. We achieve state-of-the-art quality on BEA 2019 English dataset even using weak BERT-GEC edit generator. Combining our roberta-base scorer with state-of-the-art GECToR edit generator, we surpass GECToR by 2-3%. With a larger model we establish a new SOTA on BEA development and test sets. Our model also sets a new SOTA on Russian, despite using smaller models and less data than the previous approaches.
%R 10.18653/v1/2022.emnlp-main.785
%U https://aclanthology.org/2022.emnlp-main.785
%U https://doi.org/10.18653/v1/2022.emnlp-main.785
%P 11416-11429

Markdown (Informal)

[Improved grammatical error correction by ranking elementary edits](https://aclanthology.org/2022.emnlp-main.785) (Sorokin, EMNLP 2022)

• Improved grammatical error correction by ranking elementary edits (Sorokin, EMNLP 2022)

ACL

• Alexey Sorokin. 2022. Improved grammatical error correction by ranking elementary edits. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11416–11429, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.