Abstract
In the field of education, framing right questions is an important way of measuring the knowledge of an individual, and automation of the generation of questions would reduce the strain on educators and thus increase efficiency in learning. Literature survey reveals that while there are several promising methods for testing comprehension generating questions, but none of them can be used reliably for generating meaningful questions all the time, and thus, further research is required. A novel method of generating questions is presented in this paper from input text, by encoding the answers into the model and using attention that learns the dependencies between both input text and answers and between input text and questions, thus generating questions which are relevant to the answer given. The dataset used is the SQuAD dataset, and the model produces fluent questions which have been evaluated with the required metrics.
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Kumar, A.P., Sridhar, G., Nayak, A., Shenoy, M.K. (2022). A Neural Attention Model for Automatic Question Generation Using Dual Encoders. In: Shetty D., P., Shetty, S. (eds) Recent Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, vol 1386. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_34
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