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.

Cross-Cultural Believability of Robot Characters

<p>Believability of characters is an objective in literature, theater, animation, film, and other media. Virtual characters, believable as sharing their ethnic background with users, improve their perception of the character and, sometimes, even their task performance. Social scientists refer to this phenomenon as homophily—humans tend to associate and bond with similar others. Homophily based on ethnic similarity between humans and robots, however, has not previously been tested, in part due to the difficulties of endowing a robot with ethnicity. We tackle this task by attempting to avoid blatant labels of ethnicity such as clothing, accent, or ethnic appearance (although we control for the latter), and instead aim at evoking ethnicity via more subtle verbal and nonverbal behaviors.</p> <p>Until now, when designing ethnically-specific virtual agents, their behaviors have been typically borrowed from anthropological studies and cultural models. Other approaches collect corpora of human interactions in target contexts and select maximally distinctive behaviors for further implementation on a virtual character. In this thesis, we argue that both behaviors that signal differences between an anthropologist and the target ethnicity (rich points), as well as maximally distinctive behaviors between target ethnicities, may vary on their ability to evoke ethnic attribution. We address this discrepancy by performing an additional evaluation of the candidate behaviors on their salience as ethnic cues via online crowdsourcing. The most salient ethnic cues are then implemented on the robot for a study with colocated participants.</p> <p>This methodology has allowed us to design robot characters that elicit associations between the robot’s behaviors and ethnic attributions of the characters as native speakers of American English, or native speakers of Arabic speaking English as a foreign language, by members of both of these ethnic communities. Although we did not find evidence of ethnic homophily, we believe that the suggested pathway can be used to create robot characters with a higher degree of perceived similarity, and better chances of evoking homophily effect.</p