  We present a natural language generator based on the sequence-to-sequence
approach that can be trained to produce natural language strings as well as
deep syntax dependency trees from input dialogue acts, and we use it to
directly compare two-step generation with separate sentence planning and
surface realization stages to a joint, one-step approach. We were able to train
both setups successfully using very little training data. The joint setup
offers better performance, surpassing state-of-the-art with regards to
n-gram-based scores while providing more relevant outputs.
