A Ranking Model for Evaluation of Conversation Partners Based on Rapport Levels.

Our proposed ranking model ranks conversation partners based on self-reported rapport levels for each participant. The model is important for tasks that recommend interaction partners based on user rapport built in past interactions, such as matchmaking between a student and a teacher in one-to-one online language classes. To rank conversation partners, we can use a regression model that predicts rapport ratings. It is, however, challenging to learn the mapping from the participants' behavior to their associated rapport ratings because a subjective scale for rapport ratings may vary across different participants. Hence, we propose a ranking model trained via preference learning (PL). The model avoids the subjective scale bias because the model is trained to predict ordinal relations between two conversation partners based on rapport ratings reported by the same participant. The input of the model is multimodal (acoustic and linguistic) features extracted from two participants' behaviors in an interaction. Since there is no publicly available dataset for validating the ranking model, we created a new dataset composed of online dyadic (person-to-person) interactions between a participant and several different conversation partners. We compare the ranking model trained via preference learning with the regression model by using evaluation metrics for the ranking. The experimental results show that preference learning is a more suitable approach for ranking conversation partners. Furthermore, we investigate the effect of each modality and the different stages of rapport development on the ranking performance.