QRFA: A DataDriven Model of InformationSeeking Dialogues. Understanding the structure of interaction processes helps us to improve informationseeking 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 informationseeking 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 informationseeking 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.

Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues Analysing and improving chatbot dialogues xe2x80x93 socalled chatbot xe2x80x98trainingxe2x80x99 xe2x80x93 is key to the successful implementation and maintenance of chatbots for customer service. Nevertheless, the details of this practice and what service providers may learn from the analysis of such dialogues is not investigated in current research on chatbots. As a first step towards bridging this gap in existing knowledge, we present a study of the qualitative analysis of chatbots dialogues in the context of the customer service department of a large telecom provider. In total 406 dialogues, randomly sampled from all chatbot dialogues during a fourweek period, were included in the analysis. The analysis concerned the chatbotxe2x80x99s ability to resolve customersxe2x80x99 requests, the quality in the chatbot dialogues, and suggestions for improvements of the chatbot knowledge base generated through the analysis. The findings shed light on characteristics of successful and unsuccessful chatbot dialogues and the kind of improvements that may be derived from such analysis. On the basis of the findings we summarize implications for theory and practice, and suggest future research.