Abstract
The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.
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