Building on this line of work, Zhang and Lapata (2017) combine a novel sequence-to-sequence encoder-decoder model with a deep reinforcement learning framework that rewards the system for providing simple, fluent output, similar in meaning to the input.
one of the main challenges for such comprehensive end-to-end systems is the ability to address specific types of errors independently.