Neural Approach to Automatic Subjective Question Generation System Using Multiple Filters for Supporting Correct WH-type Question. - Khandait, Khushbu R.; Bhura, Sohel A.; Asole, Suresh S. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The automatic development of subjective questions has become a crucial study area in the field of natural language processing, with enormous potential for applications in education, communication, and content creation. This study suggests a unique method for producing high-quality subjective questions that makes use of a neural architecture that has been strengthened by a number of filters. A comprehensive solution has been developed as a result of the difficulties that now exist in accurately capturing context, coherence, and emotional resonance within created questions. To maintain contextual relevance and coherence, the suggested approach combines an attention mechanism with a sequence-to-sequence neural model. Further improving question quality is the addition of grammar, context, and semantic filters that serve as guiding restrictions during question development. This research demonstrates the effectiveness of the suggested strategy in developing contextually matched, emotionally resonant, and grammatically accurate subjective questions using a mix of literature analysis, case study, and evaluation metrics such as BLEU, ROUGE, METEOR, and human evaluations. This study expands automated question creation and creates opportunities for better content engagement and interaction in a variety of applications by solving significant constraints in current approaches.