Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax   Trees and Strings
  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. 
Ond\v{r}ej Du\v{s}ek and Filip Jur\v{c}\'i\v{c}ek
cs.CL