A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge locate best way deal executing it. Three models are mostly used in applications, namely: recurrent networks (RNN), connectionist temporal classification (CTC), attention model. evidence we adopted conducting included utilizing examination inquiries or research questions determine keywords, which were search for bits peer-reviewed papers, articles, books at scholastic directories. Through introductory hunts, 790 scholarly works found, assistance choice criteria PRISMA methodology, number papers reviewed decreased 16. Every one 16 articles was categorized by their contribution each question, they broken down. At last, experienced quality appraisal where subsequent range from 83.3% 100%. proposed systematic review enabled us collect, evaluate, analyze, explore different approaches implementing pointed out most common use machine learning. followed methodology that shows potential applying these real-world applications.