Different from conventional statistical machine translation approaches, neural machine translation approaches aim at learning a radically end-to-end neural network model to optimize translation performance by generalizing machine translation as a sequence learning problem.
in reality, language pairs between English and many other target languages may not be large enough, and pivot-based SMT sometimes fails to handle this problem.