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        <title>Character Transformations for Non-Autoregressive GEC Tagging</title>
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        <namePart type="given">Milan</namePart>
        <namePart type="family">Straka</namePart>
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        <namePart type="family">Straková</namePart>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>We propose a character-based non-autoregressive GEC approach, with automatically generated character transformations. Recently, per-word classification of correction edits has proven an efficient, parallelizable alternative to current encoder-decoder GEC systems. We show that word replacement edits may be suboptimal and lead to explosion of rules for spelling, diacritization and errors in morphologically rich languages, and propose a method for generating character transformations from GEC corpus. Finally, we train character transformation models for Czech, German and Russian, reaching solid results and dramatic speedup compared to autoregressive systems. The source code is released at https://github.com/ufal/wnut2021_character_transformations_gec.</abstract>
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%0 Conference Proceedings
%T Character Transformations for Non-Autoregressive GEC Tagging
%A Straka, Milan
%A Náplava, Jakub
%A Straková, Jana
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%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 straka-etal-2021-character
%X We propose a character-based non-autoregressive GEC approach, with automatically generated character transformations. Recently, per-word classification of correction edits has proven an efficient, parallelizable alternative to current encoder-decoder GEC systems. We show that word replacement edits may be suboptimal and lead to explosion of rules for spelling, diacritization and errors in morphologically rich languages, and propose a method for generating character transformations from GEC corpus. Finally, we train character transformation models for Czech, German and Russian, reaching solid results and dramatic speedup compared to autoregressive systems. The source code is released at https://github.com/ufal/wnut2021_character_transformations_gec.
%R 10.18653/v1/2021.wnut-1.46
%U https://aclanthology.org/2021.wnut-1.46
%U https://doi.org/10.18653/v1/2021.wnut-1.46
%P 417-422

Markdown (Informal)

[Character Transformations for Non-Autoregressive GEC Tagging](https://aclanthology.org/2021.wnut-1.46) (Straka et al., WNUT 2021)

• Character Transformations for Non-Autoregressive GEC Tagging (Straka et al., WNUT 2021)

ACL

• Milan Straka, Jakub Náplava, and Jana Straková. 2021. Character Transformations for Non-Autoregressive GEC Tagging. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 417–422, Online. Association for Computational Linguistics.