Incorporating a user model to improve detection of unhelpful robot answers

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.