TY - GEN
T1 - Natural language generation as planning under uncertainty for spoken dialogue systems
AU - Rieser, Verena
AU - Lemon, Oliver
PY - 2010
Y1 - 2010
N2 - 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). The model is adaptive and incremental at the turn level, and optimises NLG actions with respect to a data-driven objective function. 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 in an objective function 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. © 2010 Springer-Verlag Berlin Heidelberg.
AB - 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). The model is adaptive and incremental at the turn level, and optimises NLG actions with respect to a data-driven objective function. 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 in an objective function 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. © 2010 Springer-Verlag Berlin Heidelberg.
KW - Adaptivity
KW - data-driven methods
KW - Incremental NLG
KW - Information Presentation
KW - Optimisation
KW - Reinforcement Learning
KW - Spoken Dialogue Systems
U2 - 10.1007/978-3-642-15573-4_6
DO - 10.1007/978-3-642-15573-4_6
M3 - Conference contribution
SN - 3642155723
SN - 9783642155727
VL - 5790 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 105
EP - 120
BT - Empirical Methods in Natural Language Generation - Data-Oriented Methods and Empirical Evaluation
T2 - 12th European Workshop on Natural Language Generation 2009
Y2 - 30 March 2009 through 3 April 2009
ER -