QRFA: A Data-Driven Model of Information-Seeking Dialogues
This work addresses the need for improved dialogue systems by providing a data-driven model for analyzing information-seeking conversations, though it is incremental as it builds on existing process mining methods.
The paper tackles the problem of understanding interaction processes in information-seeking dialogues by applying process mining techniques to conversational transcripts, resulting in the QRFA model that better reflects real conversation flows and identifies malfunctions in dialogue systems.
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.