Machine reading comprehension model based on query reconstruction technology and deep learning

Machine reading comprehension is introduced to improve machines' readability and understandability of human languages. This sophisticated version of natural language processing is used for testing and improving the machine's efficiency for reading and responding to the input texts for appropriate queries. In this article, a persistent comprehensive model using query reconstruction is introduced to address the "Cloze Style" issue in text reading. This issue results in multiple output delivery that serves as irrelevant for different input queries. Therefore, the query reconstruction using the possible combination, reducing the aforementioned issue, is introduced in this model. The possible query keywords are replaced using the maximum individual combinations. The combinations are swapped using deep learning through keyword training and substitution processes. This process is persistent until the maximum text output (answer/ response) is obtained from the machine. The output is used for analyzing the understandability of the machine based on which the training intensity is tuned for successive iterations. Therefore, the proposed model scrutinizes output accuracy by reducing errors under controlled combination time.