Recent Trends in the Use of Deep Learning Models for Grammar Error Handling

TL;DR

This survey reviews recent developments in deep learning models for grammar error detection and correction, highlighting neural machine translation and editor approaches, and discusses techniques to enhance their performance.

Contribution

It provides a comprehensive overview of DL-based methods for GEH, comparing models and outlining future research directions.

Findings

Neural machine translation models are effective for GEH.

Editor models offer alternative approaches for grammar correction.

Performance improvements are achieved through various data and training techniques.

Abstract

Grammar error handling (GEH) is an important topic in natural language processing (NLP). GEH includes both grammar error detection and grammar error correction. Recent advances in computation systems have promoted the use of deep learning (DL) models for NLP problems such as GEH. In this survey we focus on two main DL approaches for GEH: neural machine translation models and editor models. We describe the three main stages of the pipeline for these models: data preparation, training, and inference. Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline. We compare the performance of different models and conclude with proposed future directions.