Recent Trends in the Use of Deep Learning Models for Grammar Error Handling
arXiv:2009.02358
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.
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Incorporating BERT into Neural Machine Translation
- A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
- Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
- Towards Minimal Supervision BERT-based Grammar Error Correction