Automatic Question Generation from Afaan Oromo Text UsingDeepLearning

Abstract

In the modern digital era, the availability of electronic content in multiple languages has grownconsiderably. Nevertheless, generating questions from these materials remains a labor-intensiveand time-consuming process, particularly for low-resource languages like Afaan Oromo. Whilesignificant progress has been made in Automatic Question Generation (AQG) for languages suchas English, Chinese, Amharic and Somali. there is a notable lack of technology with NLPonAfaan Oromo QG because Afaan Oromo has the unique linguistic characteristics. This studybridges this gap by developing an AQG system for Afaan Oromo using deep learning models, specifically LSTM, Bi-LSTM, and GRU. To build the model a dataset of 5,000 paragraph-question-answer triples was meticulouslycurated from Afaan Oromo textbooks and educational resources. The dataset underwent preprocessing steps such as tokenization, normalization, and word embedding using Word2Vec. A deep learning model with an attention mechanism was employed to generate questions bydesign science research methodology, Among the models evaluated, the Bi-LSTMdemonstratedthe highest performance, achieving a training accuracy of 95.3% and a validation accuracyof 92.5%. The LSTM model also performed well, with a training accuracy of 92.31%and a validation accuracy of 91.02%, while the GRU model showed marked improvement after hyperparameter tuning, reaching 88.0% training accuracy and 87.0% validation accuracy The results indicate that the Bi-LSTM model is the most effective for generating both factoidandnon-factoid questions in Afaan Oromo. Future research should explore transfer learning frompre-trained models, expand the dataset through collaborations with educational institutions, andintegrate advanced neural architectures like Transformers to further enhance performance andquestion quality. This study makes a significant contribution to the field of Natural LanguageProcessing (NLP) by pioneering AQG for Afaan Oromo, use as input for QA system, providingafoundation for future research and practical applications in education and language preservation.