A Deep Learning Approach to Grammar Checking for the Gujarati Language

The application of deep learning has revolutionized Natural Language Processing (NLP), offering significant advancements over traditional rule-based systems in tasks like grammar checking. While languages like English benefit from sophisticated tools, low-resource languages like Gujarati lag due to morphological richness, ambiguity, and a scarcity of annotated datasets. This paper addresses this research gap by proposing a deep learning-based model for grammatical error detection in Gujarati. We implement a Bidirectional Long Short-Term Memory (Bi-LSTM) network, a type of Recurrent Neural Network (RNN) adept at handling sequential data. The model is trained and validated on a custom dataset to learn complex grammatical patterns specific to Gujarati, which follows a Subject-Object-Verb (SOV) structure and features extensive morphological inflection. Our experimental results demonstrate that the model achieves a validation accuracy of 97% and a validation loss of 0.2 at 75 epochs, indicating high proficiency in identifying grammatical errors. This work underscores the potential of deep learning techniques in building accurate and efficient grammar checking tools for morphologically complex Indian languages, thereby enhancing writing quality and supporting digital inclusivity.