A Comprehensive Survey of Grammar Error Correction

By Yu Wang, Yuelin Wang, Jie Liu, Zhuowei Liu. 2020

42 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Text editing is a decades-old subfield of NLP that has traditionally focused on fixing errors in machine translation (Chollampatt et al., 2020; Simard et al., 2007; Chatterjee et al., 2020) or grammar in human-written text (Wang et al., 2020b).”
FRUIT: Faithfully Reflecting Updated Information in Text (2021, background): “However, 041 existing work has mainly focused on correcting 042 grammar (Wang et al., 2020), reducing repetitive 043 typing (Chen et al., 2019), and following rhetori- 044 cal directives (Sun et al., 2021), whereas the prob- 045 lem of producing edits grounded in external knowl- 046 edge has…”