  In this paper, we describe approaches for developing Emily, an
emotion-affective open-domain chatbot. Emily can perceive a user's negative
emotion state and offer supports by positively converting the user's emotion
states. This is done by finetuning a pretrained dialogue model upon data
capturing dialogue contexts and desirable emotion states transition across
turns. Emily can differentiate a general open-domain dialogue utterance with
questions relating to personal information. By leveraging a question-answering
approach based on knowledge graphs to handle personal information, Emily
maintains personality consistency. We evaluate Emily against a few
state-of-the-art open-domain chatbots and show the effects of the proposed
approaches in emotion affecting and addressing personality inconsistency.
