Natural language generation as planning under uncertainty for spoken dialogue systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Learning to adapt to unknown users: referring expression generation in spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Optimising information presentation for spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Adaptive referring expression generation in spoken dialogue systems: evaluation with real users
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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This talk will describe new methods for generating Natural Language in interactive systems -- methods which are similar to planning approaches, but which use statistical machine learning to develop adaptive NLG components. Employing statistical models of users, generation contexts, and of Natural Languages themselves, has several potentially beneficial features: the ability to train models on real data, the availability of precise mathematical methods for optimisation, and the capacity to adapt robustly to previously unseen situations. Rather than emulating human behaviour in generation (which can be sub-optimal) these methods can even find strategies for NLG which improve upon human performance.