An intelligent neural question answer generation from text using Seq2se2 with attention mechanism system

Utilising data to its fullest extent is becoming increasingly important due to the rapid advancement of data over the past few years. Neural Question Answering is best for this much data. Question-Answer pairings have been laborious. Self-evaluation, education, and courses require questions and answers. Other AI businesses automate customer support inquiries. Designing such a system involves curating a database of consumer enquiries and live customer support representatives' responses. For a new query, the system finds the best matched response in the curated dataset. Despite lacking common sense and reasoning skills, the Question Answering System is nonetheless widely used. We propose using reading comprehension strategies to automatically generate questions from sentences. The study used several methods to find the best Question Answer Pair algorithm. To boost accuracy, the model uses BERT, ELMo, and GloVe embedding methods. The model accurately reflects semantic and syntactic characteristics of the input text using these embedding strategies. Attention mechanisms help the model focus on key inputs and generate contextual predictions. Attention and embedding improve model accuracy.