QRFA: A Data-Driven Model of Information-Seeking Dialogues
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Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model of information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our results are grounded in an empirical evaluation across multiple conversational datasets from different domains, which was never attempted before. We show that the QRFA model better reflects conversation flows observed in real information-seeking conversations than models proposed previously. Moreover, QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis.
KeywordsConversational search Log analysis Process mining
The work of S. Vakulenko and C. Di Ciccio has received funding from the EU H2020 program under MSCA-RISE agreement 645751 (RISE_BPM) and the Austrian Research Promotion Agency (FFG) under grant 861213 (CitySPIN). S. Vakulenko was also supported by project 855407 “Open Data for Local Communities” (CommuniData) of the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under the program “ICT of the Future.” M. de Rijke was supported by Ahold Delhaize, the Association of Universities in the Netherlands (VSNU), and the Innovation Center for Artificial Intelligence (ICAI).
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