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. .
