We study knowledge-grounded dialogue generation with pre-trained language models. Instead of pursuing new state-of-the-art on benchmarks, we try to understand if the knowledge stored in parameters of the pre-trained models is already enough to ground open domain dialogues, and thus allows us to get rid of the dependency on external knowledge sources in generation. Through extensive experiments on benchmarks, we find that by fine-tuning with a few dialogues containing knowledge, the pre-trained language models can outperform the state-of-the-art model that requires external knowledge in automatic evaluation and human judgment, suggesting a positive answer to the question we raised.

翻译：我们研究以经过培训的语言模式进行基于知识的对话。 我们试图理解,在经过培训的模型参数中储存的知识是否已经足以建立公开的域对话,从而使我们能够摆脱对外部知识来源的依赖。 通过对基准的广泛实验,我们发现,通过微调包含知识的少数对话,经过培训的语言模式能够超过在自动评估和人类判断方面需要外部知识的先进模式,这为我们提出的问题提供了积极的答案。