  We present and evaluate a new model for Natural Language Generation (NLG) in
Spoken Dialogue Systems, based on statistical planning, given noisy feedback
from the current generation context (e.g. a user and a surface realiser). We
study its use in a standard NLG problem: how to present information (in this
case a set of search results) to users, given the complex trade- offs between
utterance length, amount of information conveyed, and cognitive load. We set
these trade-offs by analysing existing MATCH data. We then train a NLG pol- icy
using Reinforcement Learning (RL), which adapts its behaviour to noisy feed-
back from the current generation context. This policy is compared to several
base- lines derived from previous work in this area. The learned policy
significantly out- performs all the prior approaches.
