  Traditional methods for deep NLG adopt pipeline approaches comprising stages
such as constructing syntactic input, predicting function words, linearizing
the syntactic input and generating the surface forms. Though easier to
visualize, pipeline approaches suffer from error propagation. In addition,
information available across modules cannot be leveraged by all modules. We
construct a transition-based model to jointly perform linearization, function
word prediction and morphological generation, which considerably improves upon
the accuracy compared to a pipelined baseline system. On a standard deep input
linearization shared task, our system achieves the best results reported so
far.
