Visual Question Generation on VQA Dataset
Résumé
Question Generation in reading comprehension has garnered significant interest recently. Traditionally, most research in this area has focused on generating questions from textual information related to the reading passage using sequence-to-sequence models. In this work, we explored an alternative approach by investigating the generation of questions solely from images that may accompany a reading passage. This novel approach involves leveraging a deep learning model that combines VGG (Visual Geometry Group) and LSTM (Long Short-Term Memory) networks in a serial architecture. By training the model on a Visual Question Answering (VQA) dataset, it has been demonstrated that the system can effectively generate insightful and contextually relevant questions from images sourced from the internet. This research highlights the potential for integrating visual content into the question generation process, offering new avenues for enhancing reading comprehension tasks.
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