An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Developing and empirically evaluating robust explanation generators: the KNIGHT experiments
Computational Linguistics
An empirical study on the generation of anaphora in Chinese
Computational Linguistics
ANLC '92 Proceedings of the third conference on Applied natural language processing
A fast and portable realizer for text generation systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
SPoT: a trainable sentence planner
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Journal of Artificial Intelligence Research
Natural language generation in dialog systems
HLT '01 Proceedings of the first international conference on Human language technology research
Corpus-based methods in natural language generation: friend or foe?
EWNLG '01 Proceedings of the 8th European workshop on Natural Language Generation - Volume 8
Trainable sentence planning for complex information presentation in spoken dialog systems
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACM Transactions on Speech and Language Processing (TSLP)
Individual and domain adaptation in sentence planning for dialogue
Journal of Artificial Intelligence Research
Towards personality-based user adaptation: psychologically informed stylistic language generation
User Modeling and User-Adapted Interaction
Controlling user perceptions of linguistic style: Trainable generation of personality traits
Computational Linguistics
Question ranking and selection in tutorial dialogues
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.