TSD 2003: Text, Speech and Dialogue pp 138-145 | Cite as
Optimising Attribute Selection in Conversational Search
Conference paper
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.
Keywords
Search Space User Knowledge Attribute Selection Dialogue System Simulated User
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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