The TREC question answering track
Natural Language Engineering
Neurocomputing
Interactive question answering and constraint relaxation in spoken dialogue systems
Natural Language Engineering
Building effective question answering characters
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Ada and grace: toward realistic and engaging virtual museum guides
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Learning dialogue strategies from older and younger simulated users
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling spoken decision making dialogue and optimization of its dialogue strategy
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
User Modeling and User-Adapted Interaction
Learning culture-specific dialogue models from non culture-specific data
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: users diversity - Volume Part II
Reinforcement learning for parameter estimation in statistical spoken dialogue systems
Computer Speech and Language
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We use Reinforcement Learning (RL) to learn question-answering dialogue policies for a real-world application. We analyze a corpus of interactions of museum visitors with two virtual characters that serve as guides at the Museum of Science in Boston, in order to build a realistic model of user behavior when interacting with these characters. A simulated user is built based on this model and used for learning the dialogue policy of the virtual characters using RL. Our learned policy outperforms two baselines (including the original dialogue policy that was used for collecting the corpus) in a simulation setting.