  Responses in task-oriented dialogue systems often realize multiple
propositions whose ultimate form depends on the use of sentence planning and
discourse structuring operations. For example a recommendation may consist of
an explicitly evaluative utterance e.g. Chanpen Thai is the best option, along
with content related by the justification discourse relation, e.g. It has great
food and service, that combines multiple propositions into a single phrase.
While neural generation methods integrate sentence planning and surface
realization in one end-to-end learning framework, previous work has not shown
that neural generators can: (1) perform common sentence planning and discourse
structuring operations; (2) make decisions as to whether to realize content in
a single sentence or over multiple sentences; (3) generalize sentence planning
and discourse relation operations beyond what was seen in training. We
systematically create large training corpora that exhibit particular sentence
planning operations and then test neural models to see what they learn. We
compare models without explicit latent variables for sentence planning with
ones that provide explicit supervision during training. We show that only the
models with additional supervision can reproduce sentence planing and discourse
operations and generalize to situations unseen in training.
