Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation

Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation

Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4279-4285. https://doi.org/10.24963/ijcai.2018/595

Endowing a chatbot with personality is challenging but significant to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified personality or profile. We present a method that uses generic conversation data from social media (without speaker identities) to generate profile-coherent responses. The central idea is to detect whether a profile should be used when responding to a user post (by a profile detector), and if necessary, select a key-value pair from the profile to generate a response forward and backward (by a bidirectional decoder) so that a personality-coherent response can be generated. Furthermore, in order to train the bidirectional decoder with generic dialogue data, a position detector is designed to predict a word position from which decoding should start given a profile value. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.
Keywords:
Natural Language Processing: Dialogue
Natural Language Processing: Natural Language Generation