Neural Approach to Automatic Subjective Question Generation System Using Multiple Filters for Supporting Correct WH-type Question.

Authors

  • Khushbu R. Khandait Research scholar, Babasaheb Naik College of Engineering, Department of Computer Science and Engineering Sant Gadge Baba Amravati University, Pusad, Amravati, India - 445204
  • Sohel A. Bhura Associate Professor, Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, India - 440002
  • Suresh S. Asole Head & Associate Professor, Department of Computer Science and Engineering, Babasaheb Naik College of Engineering, Pusad, India- 445204

Keywords:

Neural Approach, Question Generation, Subjective Questions, Multiple Filters, Attention Mechanism, Coherence, Contextual Relevance, Emotional Resonance, Grammatical Correctness

Abstract

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.

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Published

06.09.2023

How to Cite

Khandait, K. R. ., Bhura, S. A. ., & Asole, S. S. . (2023). Neural Approach to Automatic Subjective Question Generation System Using Multiple Filters for Supporting Correct WH-type Question. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 60–72. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3435

Issue

Vol. 11 No. 11s (2023)

Section

Research Article

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