Knowledge-grounded dialogue is a task of generating an informative response based on external knowledge,mainly including query generation for knowledge retrieval and knowledge-grounded dialogue generation.This paper presents a cascading-architecture and multi-model fusion based knowledge-grounded dialogue system.For query generation task,this paper proposes a cascade decoupling strategy,that is,knowledge retrieval query genera-tion task is divided into a knowledge retrieval discrimination task and a retrieval query generation task.In order to improve the consistency and diversity of dialogue,this paper introduces a variety of dialogue training strategies after dialogue pre-training to establish several competitive dialogue models with high qualities.For the responses generated by different dialogue models,this paper proposes a re-ranking strategy based on mutual voting.The sys-tem proposed in this paper ranked the 1st in the automatic evaluation and 3rd in the human evaluation in the"2022 language and intelligent technology competition:knowledge dialogue track".
维普
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