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.
Cite
CITATION STYLE
Zheng, J., Napoles, C., Tetreault, J., & Omelianchuk, K. (2018). How do you correct run-on sentences it’s not as easy as it seems. In 4th Workshop on Noisy User-Generated Text, W-NUT 2018 - Proceedings of the Workshop (pp. 33–38). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-6105
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.