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As the usage of service robots becomes more sophisticated, direct communication by means of human language is required to increase the efficiency of their performance. In natural speech interaction, however, people often omit some words and rely on background knowledge or the context, resulting in ambiguity. In order to develop smarter service robots, therefore, managing the context of interaction is essential. In this correspondence, we have investigated the mixed-initiative interaction that prompts for missing information and clarifies ambiguous statements based on hierarchically designed Bayesian networks. Simulation with the Kephera II robot and a usability test have demonstrated the usefulness of the proposed method.