A four-stage framework (convertibility check, question/reason generation, optimal pair selection, and template-based image generation) produces MCQs with image options from ScienceQA content.
Question Generation from a Knowledge Base with Web Exploration
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abstract
Question generation from a knowledge base (KB) is the task of generating questions related to the domain of the input KB. We propose a system for generating fluent and natural questions from a KB, which significantly reduces the human effort by leveraging massive web resources. In more detail, a seed question set is first generated by applying a small number of hand-crafted templates on the input KB, then more questions are retrieved by iteratively forming already obtained questions as search queries into a standard search engine, before finally questions are selected by estimating their fluency and domain relevance. Evaluated by human graders on 500 random-selected triples from Freebase, questions generated by our system are judged to be more fluent than those of \newcite{serban-EtAl:2016:P16-1} by human graders.
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2025 1verdicts
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Beyond the Textual: Generating Coherent Visual Options for MCQs
A four-stage framework (convertibility check, question/reason generation, optimal pair selection, and template-based image generation) produces MCQs with image options from ScienceQA content.