Vakulenko, Svitlana, Revoredo, Kate, Di Ciccio, Claudio, de Rijke, Maarten. 2019. QRFA: A Data-Driven Model of Information-Seeking Dialogues. In 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I, Hrsg. Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, Djoerd Hiemstra, 541-557. Cologne, Germany: Springer.

RIS

@INPROCEEDINGS{Vakulenko2019,
title = {QRFA: A Data-Driven Model of Information-Seeking Dialogues},
author = {Svitlana Vakulenko and Kate Revoredo and Claudio Di Ciccio and Maarten de Rijke},
publisher = {Springer},
year = {2019},
address = {Cologne, Germany},
url = {https://doi.org/10.1007/978-3-030-15712-8_35},
publisher = {Springer},
language = {EN},
pages = {541-557},
series = {41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I},
abstract = {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.},
}