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        <title>Hierarchical Character Tagger for Short Text Spelling Error Correction</title>
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    <name type="personal">
        <namePart type="given">Mengyi</namePart>
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    <name type="personal">
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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    <abstract>State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.13</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Hierarchical Character Tagger for Short Text Spelling Error Correction
%A Gao, Mengyi
%A Xu, Canran
%A Shi, Peng
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F gao-etal-2021-hierarchical
%X State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.
%R 10.18653/v1/2021.wnut-1.13
%U https://aclanthology.org/2021.wnut-1.13
%U https://doi.org/10.18653/v1/2021.wnut-1.13
%P 106-113

Markdown (Informal)

[Hierarchical Character Tagger for Short Text Spelling Error Correction](https://aclanthology.org/2021.wnut-1.13) (Gao et al., WNUT 2021)

• Hierarchical Character Tagger for Short Text Spelling Error Correction (Gao et al., WNUT 2021)

ACL

• Mengyi Gao, Canran Xu, and Peng Shi. 2021. Hierarchical Character Tagger for Short Text Spelling Error Correction. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 106–113, Online. Association for Computational Linguistics.