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.

本文采用过程挖掘技术应用于对话记录中，从多个不同领域的交互式数据集中提取新的 QRFA 信息寻求对话模型，该模型更好地反映了实际信息寻求对话中观察到的对话流程，并且通过符合性分析，使我们能够识别对话系统中的故障。