Analysis of modern machine learning methods for detecting grammatical errors

Abstract

In recent years, with the rapid development of the natural language processing industry, the problem of improving human spelling has become increasingly important. This is especially important in the context of automating text checking tasks, machine translation, search queries, and the creation of word processors. This article discusses the main approaches to correcting grammatical errors, including rule-based methods, statistical models, hybrid methods and transformers. Particular attention is paid to neural networks, which demonstrate exponential growth in accuracy due to technologies such as the attention mechanism and Transformer models. The advantages and disadvantages of the main types of machine translation, such as statistical, hybrid and neural translation, are revealed. Modern evaluation metrics, such as BLEU, METEOR and WER, and their adaptation to morphologically rich languages, including Ukrainian, are discussed. The paper provides a comparative analysis of the models' performance in terms of accuracy, speed, and ability to work with context, making it an important resource for NLP researchers and engineers.