In this study, we formalize a novel task of Response Generation for Multi-Party Chatbots (RGMPC) and propose an end-to-end model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (I-CRED) for RGMPC.
we employ interactive speaker models to capture contextual interlocutor information.