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.

本研究提出了一种新的基于统计规划的自然语言生成模型，它能够在存在噪声反馈的情况下（例如用户和实现器的当前生成上下文），有效地解决常见的自然语言生成问题，包括信息展示、语句长度和信息传递量等方面的折衷问题。我们采用强化学习方法训练该模型，并将其性能与先前工作中的基准性能进行了比较，结果表明，该模型的性能明显优于所有基线模型。