  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.
