A Review of Deep Learning-Based Question Generation: Challenges, Methods, and Future Directions
TL;DR
This review critically surveys the evolution of QG techniques from early rule-based and statistical models to modern neural architecture such as sequence-to-sequence models, attention-based frameworks, and transformer-based systems like BERT, GPT, T5, and BART to guide researchers and practitioners in developing more robust, adaptable, and ethically sound QG systems.
Abstract
Deep Learning-based Question Generation (QG) has become an essential subfield in natural language processing (NLP), with transformative implications across education, conversational AI, and information retrieval. This review critically surveys the evolution of QG techniques from early rule-based and statistical models to modern neural architecture such as sequence-to-sequence models, attention-based frameworks, and transformer-based systems like BERT, GPT, T5, and BART. Key contributions of this paper include a comparative analysis of traditional and deep learning-based approaches, a comprehensive overview of datasets and benchmarks, and a discussion of evaluation metrics encompassing lexical, semantic, and human-centered dimensions. The review further explores the integration of reinforcement learning and multimodal capabilities in state-of-the-art QG systems. It identifies pressing challenges such as semantic coherence, hallucination, bias, evaluation limitations, and domain dependence. By synthesizing recent advances and highlighting future research directions such as zero-shot learning, domain adaptation, and human-in-the-loop training this paper aims to guide researchers and practitioners in developing more robust, adaptable, and ethically sound QG systems. The work serves not only as a resource for understanding current capabilities and limitations but also as a roadmap for advancing the field toward more context-aware, customizable, and high-utility applications.