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Báo cáo khoa học: "Using Machine Learning to Explore Human Multimodal Clarification Strategies"

We investigate the use of machine learning in combination with feature engineering techniques to explore human multimodal clarification strategies and the use of those strategies for dialogue systems. We learn from data collected in a Wizardof-Oz study where different wizards could decide whether to ask a clarification request in a multimodal manner or else use speech alone. We show that there is a uniform strategy across wizards which is based on multiple features in the context. These are generic runtime features which can be implemented in dialogue systems. . | Using Machine Learning to Explore Human Multimodal Clarification Strategies Verena Rieser Department of Computational Linguistics Saarland University Saarbrucken D-66041 vrieser@coli.uni-sb.de Oliver Lemon School of Informatics University of Edinburgh Edinburgh EH8 9LW Gb olemon@inf.ed.ac.uk Abstract We investigate the use of machine learning in combination with feature engineering techniques to explore human multimodal clarification strategies and the use of those strategies for dialogue systems. We learn from data collected in a Wizard-of-Oz study where different wizards could decide whether to ask a clarification request in a multimodal manner or else use speech alone. We show that there is a uniform strategy across wizards which is based on multiple features in the context. These are generic runtime features which can be implemented in dialogue systems. Our prediction models achieve a weighted f-score of 85.3 which is a 25.5 improvement over a one-rule baseline . To assess the effects of models feature discretisation and selection we also conduct a regression analysis. We then interpret and discuss the use of the learnt strategy for dialogue systems. Throughout the investigation we discuss the issues arising from using small initial Wizard-of-Oz data sets and we show that feature engineering is an essential step when learning from such limited data. 1 Introduction Good clarification strategies in dialogue systems help to ensure and maintain mutual understanding and thus play a crucial role in robust conversational interaction. In dialogue application domains with high interpretation uncertainty for example caused by acoustic uncertainties from a speech recogniser multimodal generation and input leads to more robust interaction Oviatt 2002 and re duced cognitive load Oviatt et al. 2004 . In this paper we investigate the use of machine learning ML to explore human multimodal clarification strategies and the use of those strategies to decide based on the current .