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Creators/Authors contains: "Hernandez, Stephanie"

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  1. ; (, Proceedings of the 25th Workshop on the Semantics and Pragmatics of Dialogue - Poster Abstracts)
    This paper compares methods to select data for annotation in order to improve a classifier used in a question-answering dialogue system. With a classifier trained on 1,500 questions, adding 300 training questions on which the classifier is least confident results in consistently improved performance, whereas adding 300 arbitrarily selected training questions does not yield consistent improvement, and sometimes even degrades performance. The paper uses a new method for comparative evaluation of classifiers for dialogue, which scores each classifier based on the number of appropriate responses retrieved. 
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    Accepted Manuscript Full Text Available