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        <title>How do you correct run-on sentences it’s not as easy as it seems</title>
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        <namePart type="given">Junchao</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.</abstract>
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%0 Conference Proceedings
%T How do you correct run-on sentences it’s not as easy as it seems
%A Zheng, Junchao
%A Napoles, Courtney
%A Tetreault, Joel
%A Omelianchuk, Kostiantyn
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zheng-etal-2018-correct
%X Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.
%R 10.18653/v1/W18-6105
%U https://aclanthology.org/W18-6105
%U https://doi.org/10.18653/v1/W18-6105
%P 33-38

Markdown (Informal)

[How do you correct run-on sentences it’s not as easy as it seems](https://aclanthology.org/W18-6105) (Zheng et al., WNUT 2018)

• How do you correct run-on sentences it’s not as easy as it seems (Zheng et al., WNUT 2018)

ACL

• Junchao Zheng, Courtney Napoles, Joel Tetreault, and Kostiantyn Omelianchuk. 2018. How do you correct run-on sentences it’s not as easy as it seems. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 33–38, Brussels, Belgium. Association for Computational Linguistics.