While there have been many attempts to estimate the emotion of an addresser from her/his utterance, few studies have explored how her/his utterance affects the emotion of the addressee. This has motivated us to investigate two novel tasks: predicting the emotion of the addressee and generating a response that elicits a specific emotion in the addressee's mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the predictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers.
REF presented a conversational model that generates a response so that it elicits a certain emotion (e.g., joy) in the addressee mind.
Predicting and Eliciting Addressee's Emotion in Online Dialogue
ACL
Computer Science