Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and demonstrate their complementarity.
the machine translation method learns from parallel data without requiring further linguistic input and is better at correcting complex mistakes.