Geéz Grammar Error Handling Using Neural Machine Translation Approach

The goal of natural language processing (NLP), which has recently gained popularity, is to improve the capacity of computers to comprehend and interact with human language. Consequently, to converse using natural language, it is crucial that spoken language be grammatically correct, especially for Geéz language. Geéz language sentences must follow certain norms of agreement in terms of number, person, gender, tense, and other factors to be considered grammatically correct. If the input sentence in Geéz language is improper, then it can have problems with subject-verb agreement, object-verb agreement, adjective-noun agreement, and adverb-verb agreement. The goal of the proposed work is to provide a neural machine translation approach for detecting and correcting grammar errors in Geéz sentences. We have prepared manually 11,490 Geéz parallel corpuses (Geéz language grammatically incorrect and grammatically correct sentences). After we have prepared a parallel Geéz sentence, we have used normalization, tokenization, padding, and one hot encoding as preprocesses. We have used two deep learning algorithms, including a bidirectional long short-term memory encoder-decoder and a long short-term memory encoder-decoder, for training the proposed model. Keras and TensorFlow were used for importing the required libraries, and we used the Python 3.7 environment for implementation. Two test cases are used for the evaluation technique. The first one is for the long short-term memory encoder-decoder model, and the second one is for the bidirectional long short-term memory encoder-decoder model. Finally, the bidirectional long short-term memory encoder-decoder model achieved best results with an accuracy of 82%, recall of 82%, precision of 85%, and F1-measure of 83% with balanced error type classes.