Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains a encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion.
the simple encoderdecoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge.