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Nowadays, open-domain dialogue models can generate acceptable responsesaccording to the historical context based on the large-scale pre-trainedlanguage models. However, they generally concatenate the dialogue historydirectly as the model input to predict the response, which we named as the flatpattern and ignores the dynamic information flow across dialogue utterances. Inthis work, we propose the DialoFlow model, in which we introduce a dynamic flowmechanism to model the context flow, and design three training objectives tocapture the information dynamics across dialogue utterances by addressing thesemantic influence brought about by each utterance in large-scale pre-training.Experiments on the multi-reference Reddit Dataset and DailyDialog Datasetdemonstrate that our DialoFlow significantly outperforms the DialoGPT on thedialogue generation task. Besides, we propose the Flow score, an effectiveautomatic metric for evaluating interactive human-bot conversation qualitybased on the pre-trained DialoFlow, which presents high chatbot-levelcorrelation ($r=0.9$) with human ratings among 11 chatbots. Code andpre-trained models will be public.\footnote{\url{https://github.com/ictnlp/DialoFlow}}

Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances