A Comprehensive Survey of Grammatical Error Correction

By Yu Wang, Yuelin Wang, Kai Dang, Jie Liu, Zhuowei Liu. 2021

86 citations recorded by the source corpus.

Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. We first give the definition of GEC task and introduce the public datasets and data annotation schema. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Finally, we discuss five prospective directions for future GEC researches.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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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): “Thus, several studies explore modifying LM training objectives to incorporate factual information using either knowledge bases (KBs) or graphs (Yu et al., 2022): each token prediction is scored not only on its likelihood given the previously generated context, but also on whether the generation is g”
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024, methodology): “For instance, SAD (Sun et al., 2021), an early attempt at Speculative Decoding on Grammatical Error Correction, utilized the input sentence with grammatical errors as a draft and leveraged the LLM to verify the whole sentence in parallel, achieving a 9 ×∼ 12 × speedup.”