How do you correct run-on sentences it’s not as easy as it seems

Junchao Zheng, Courtney Napoles, Joel Tetreault, Kostiantyn Omelianchuk


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.
Anthology ID:
W18-6105
Volume:
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–38
Language:
URL:
https://aclanthology.org/W18-6105
DOI:
10.18653/v1/W18-6105
Bibkey:
Cite (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.
Cite (Informal):
How do you correct run-on sentences it’s not as easy as it seems (Zheng et al., 2018)
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PDF:
https://aclanthology.org/W18-6105.pdf