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Dialog Learning in Conversational CBR Mingyang Gu and Agnar Aamodt
 

Summary: Dialog Learning in Conversational CBR
Mingyang Gu and Agnar Aamodt
Department of Computer and Information Science, Norwegian University of Science
and Technology, Sem Saelands vei 7-9, N-7491, Trondheim, Norway
Email: {mingyang, agnar}@idi.ntnu.no
Abstract
Conversational Case-Based Reasoning (CCBR) provides a
mixed-initiative dialog for guiding users to refine their prob-
lem descriptions incrementally through a question-answering
sequence. In this paper, we argue that the successful dialogs
in CCBR can be captured and learned in order to improve the
efficiency of CCBR from the perspective of shortening the
dialog length. A framework for dialog learning in CCBR is
proposed in the present paper, and an instance of this frame-
work is implemented and tested empirically in an attempt to
evaluate the learning effectiveness of the framework. The re-
sults show us that on 29 out of the 32 selected datasets, CCBR
with the dialog learning mechanism uses fewer dialog ses-
sions to retrieve the correct case than CCBR without using
dialog learning.

  

Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology

 

Collections: Computer Technologies and Information Sciences