  Each utterance in multi-turn empathetic dialogues has features such as
emotion, keywords, and utterance-level meaning. Feature transitions between
utterances occur naturally. However, existing approaches fail to perceive the
transitions because they extract features for the context at the coarse-grained
level. To solve the above issue, we propose a novel approach of recognizing
feature transitions between utterances, which helps understand the dialogue
flow and better grasp the features of utterance that needs attention. Also, we
introduce a response generation strategy to help focus on emotion and keywords
related to appropriate features when generating responses. Experimental results
show that our approach outperforms baselines and especially, achieves
significant improvements on multi-turn dialogues.
