  Grammar error correction (GEC) is an important application aspect of natural
language processing techniques. The past decade has witnessed significant
progress achieved in GEC for the sake of increasing popularity of machine
learning and deep learning, especially in late 2010s when near human-level GEC
systems are available. However, there is no prior work focusing on the whole
recapitulation of the progress. We present the first survey in GEC for a
comprehensive retrospect of the literature in this area. We first give the
introduction of five public datasets, data annotation schema, two important
shared tasks and four standard evaluation metrics. More importantly, we discuss
four kinds of basic approaches, including statistical machine translation based
approach, neural machine translation based approach, classification based
approach and language model based approach, six commonly applied performance
boosting techniques for GEC systems and two data augmentation methods. Since
GEC is typically viewed as a sister task of machine translation, many GEC
systems are based on neural machine translation (NMT) approaches, where the
neural sequence-to-sequence model is applied. Similarly, some performance
boosting techniques are adapted from machine translation and are successfully
combined with GEC systems for enhancement on the final performance.
Furthermore, we conduct an analysis in level of basic approaches, performance
boosting techniques and integrated GEC systems based on their experiment
results respectively for more clear patterns and conclusions. Finally, we
discuss five prospective directions for future GEC researches.
