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 conceptguided 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.

With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization.Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries.Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles.To bridge the gap between out-of-domain pretraining and indomain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data.Specifically, we exploit large-scale in-domain nonsummary data to separately pretrain the dialogue encoder and the summary decoder.The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization.The experimental results on two public datasets show that with only limited training data, our approach achieves competitive performance and generalizes well in different dialogue scenarios.