  Grammatical Error Correction (GEC) aims to automatically detect and correct
grammatical errors. In this aspect, dominant models are trained by
one-iteration learning while performing multiple iterations of corrections
during inference. Previous studies mainly focus on the data augmentation
approach to combat the exposure bias, which suffers from two drawbacks. First,
they simply mix additionally-constructed training instances and original ones
to train models, which fails to help models be explicitly aware of the
procedure of gradual corrections. Second, they ignore the interdependence
between different types of corrections. In this paper, we propose a Type-Driven
Multi-Turn Corrections approach for GEC. Using this approach, from each
training instance, we additionally construct multiple training instances, each
of which involves the correction of a specific type of errors. Then, we use
these additionally-constructed training instances and the original one to train
the model in turn. Experimental results and in-depth analysis show that our
approach significantly benefits the model training. Particularly, our enhanced
model achieves state-of-the-art single-model performance on English GEC
benchmarks. We release our code at Github.
