Ranking Conversation Partner based on Rapport Built in Past Conversation

Abstract

Rapport is a harmonious relationship with others. The high rapport between speakers improves the quality of social interaction. We formulate rapport estimation as learning to rank and propose a model that ranks conversational partners based on levels of rapport. This model enables users to re-match with a high rapport partner from a set of people with whom the user has communicated in the past in online language lessons or games using voice chat. Regression models that directly predict rapport ratings can be used to estimate the ranking of conversation partners. However, since rapport annotation is a subjective evaluation, it is biased due to individual differences in perceiver effects, such as response style and positivity effect. On the other hand, the proposed model avoids the problem of perceiver effects by using preference learning, which learns the ordinal relationship between two conversational partners based on the rapport reported by the same user. We compared the proposed model with the regression model using evaluation metrics for ranking. The result indicates that the proposed model is more suitable than the regression model for this task.