Abstract
It has been shown that user modelling has the potential to improve the performance of conversational search systems, particularly in what concerns the problem of attribute selection, i.e., determining which attribute to ask the user at each step of the dialogue. In this paper we present a novel framework for attribute selection which allows the fine-tuning of the relative importance of profile-based and entropy-based heuristics. Based on this framework, we describe a number of experiments which allow us to quantify the bounds to such improvements.
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Teixeira, D., Verhaegh, W. (2003). Optimising Attribute Selection in Conversational Search. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2003. Lecture Notes in Computer Science(), vol 2807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39398-6_20
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DOI: https://doi.org/10.1007/978-3-540-39398-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20024-6
Online ISBN: 978-3-540-39398-6
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