期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：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 tradeoffs
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 policy
using Reinforcement Learning (RL),
which adapts its behaviour to noisy feedback
from the current generation context.
This policy is compared to several baselines
derived from previous work in this
area. The learned policy significantly outperforms
all the prior approaches.
