Automatic Question Generation through Word Vector Synchronization using Lamma

Abstract

According to previous research, the student’s activities and participation are more significant than the visualization's content. One way to persuade students to communicate with a visual screen is to ask them to predict questions. This has been shown to assist students in learning. During algorithm exhibition, depending on the engagement category and the performance of question-response, we propose creating an autonomous question-generating system for interactive and deep learning. We demonstrate how to include more promising automatic question generation into the conventional design in this study, giving an entirely new approach to our system which is sentiment-based analysis. We also provide a set of hypothetical questions that may be generated mechanically founded on the data collected throughout the Generation. The findings and improvements in Automatic Generation accuracy with increased Blue and meteor scores are also shown in this paper.