  Human dialogue contains evolving concepts, and speakers naturally associate
multiple concepts to compose a response. However, current dialogue models with
the seq2seq framework lack the ability to effectively manage concept
transitions and can hardly introduce multiple concepts to responses in a
sequential decoding manner. To facilitate a controllable and coherent dialogue,
in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for
open-domain dialogue generation. The proposed model comprises a multi-concept
planning module that learns to identify multiple associated concepts from a
concept graph and a customized Insertion Transformer that performs
concept-guided non-autoregressive generation to complete a response. The
experimental results on two public datasets show that CG-nAR can produce
diverse and coherent responses, outperforming state-of-the-art baselines in
both automatic and human evaluations with substantially faster inference speed.
