Automatic Question Generation from Indonesian Texts Using Text-to-Text Transformers
Mukhlish Fuadi, Adhi Dharma Wibawa · 2022
Answering questions is one method to increase or measure understanding. However, creating relevant and answerable questions from the given context is not easy. Automatic Question Generation (AQG) is a part of Natural Language Processing (NLP) which can generate questions automatically from text input. Many studies related to AQG have been carried out but are still very limited in Indonesian texts, especially those that use the latest Transformer variations. This study proposes an AQG system that utilizes the latest power Transformer, the multilingual Text-to-Text Transfer Transformer (mT5). We fine-tune the mT5 model to extract answers from context and generate questions based on those answers. We use the Indonesian dataset extracted from the TyDiQA dataset and evaluate this model against the TyDiQA validation set using BLEU (BiLingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics. This model achieved BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE-L scores of 36.54, 28.24, 22.61, 18.44, and 39.57, respectively. Our model performs well and generates questions in understandable Indonesian with good word choice and grammar based on manual validation.