Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer

Abstract

Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation (NQG) has evolved, although it has restrictions on how questions can be expressed in different ways and has a finite capacity for question generation. In this paper, we propose an ensemble-type NQG model based on the text-to-text transfer transformer (T5). Through the proposed model, the number of generated questions for each single NQG model can be greatly increased by considering the mutual similarity and the quality of the questions using the soft-voting method. For the training of the soft-voting algorithm, the evaluation score and mutual similarity score weights based on the context and the question–answer (QA) dataset are used as the threshold weight. Performance comparison results with existing T5-based NQG models using the SQuAD 2.0 dataset demonstrate the effectiveness of the proposed method for QG. The implementation of the proposed ensemble model is anticipated to span diverse industrial fields, including interactive chatbots, robotic process automation (RPA), and Internet of Things (IoT) services in the future.

Keywords

• neural question generation
• deep learning
• natural language processing
• ensemble algorithm