Hala is a bilingual (Arabic and English) culturally-sensitive robot receptionist located at Carnegie Mellon University in Qatar. We report results from Hala's deployment by comparing her English dialogue corpus to that of a similar monolingual robot (named "Tank") located at CMU's Pittsburgh campus. Specifically, we compare the average number of turns per interaction, duration of interactions, frequency of interactions with personal questions, rate of non-understandings, and rate of thanks after the robot's answer. We provide possible explanations for observed similarities and differences and highlight potential cultural implications on the interactions.

Similarly, User models can be useful for improving dialogue management. In this paper we analyze human-robot dialogues that occur during uncontrolled interactions and estimate relations between the initial dialogue turns and patterns of discourse that are indicative of such user traits as persistence and politeness. The significant effects shown in this preliminary study suggest that initial dialogue turns may be useful in modeling a user's interaction style.

Dialogues with robots frequently exhibit social dialogue acts such as greeting, thanks, and goodbye. This opens the opportunity of using these dialogue acts for dialogue management, in particular for detecting misunderstandings. Our corpus analysis shows that the social dialogue acts have different scopes of their associations with the discourse features within the dialogue: greeting in the user's first turn is associated with such distant, or global, features as the likelihood of having questions answered, persistence, and ending with bye. The user's thanks turn, on the other hand, is strongly associated with the helpfulness of the preceding robot's answer. We therefore interpret the greeting as a component of a user model that can provide information about the user's traits and be associated with discourse features at various stages of the dialogue. We conduct a detailed analysis of the user's thanking behavior and demonstrate that user's thanks can be used in the detection of unhelpful robot's answers. Incorporating the greeting information further improves the detection. We discuss possible applications of this work for human-robot dialogue management.