Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
Unifying instance-based and rule-based induction
Machine Learning
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Learning with Nested Generalized Exemplars
Learning with Nested Generalized Exemplars
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Predicting automatic speech recognition performance using prosodic cues
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Predicting user reactions to system error
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Identifying user corrections automatically in spoken dialogue systems
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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Previous work has argued that memory-based learning is better than abstraction-based learning for a set of language learning tasks. In this paper, we first attempt to generalize these results to a new set of language learning tasks from the area of spoken dialog systems and to a different abstraction-based learner. We then examine the utility of various exceptionality measures for predicting where one learner is better than the other. Our results show that generalization of previous results to our tasks is not so obvious and some of the exceptionality measures may be used to characterize the performance of our learners.