  Perception and expression of emotion are key factors to the success of
dialogue systems or conversational agents. However, this problem has not been
studied in large-scale conversation generation so far. In this paper, we
propose Emotional Chatting Machine (ECM) that can generate appropriate
responses not only in content (relevant and grammatical) but also in emotion
(emotionally consistent). To the best of our knowledge, this is the first work
that addresses the emotion factor in large-scale conversation generation. ECM
addresses the factor using three new mechanisms that respectively (1) models
the high-level abstraction of emotion expressions by embedding emotion
categories, (2) captures the change of implicit internal emotion states, and
(3) uses explicit emotion expressions with an external emotion vocabulary.
Experiments show that the proposed model can generate responses appropriate not
only in content but also in emotion.
