How do you correct run-on sentences it's not as easy as it seems
This work addresses a specific grammatical error correction problem for natural language processing applications, but it is incremental as it builds on existing methods and focuses on a narrow task.
The paper tackles the problem of correcting run-on sentences, a common grammatical error with limited prior research, by introducing two machine learning models that outperform existing methods for related tasks like punctuation restoration and grammatical error correction.
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.