论文标题
通过预训练的语言模型的知识接地对话生成
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
论文作者
论文摘要
我们使用预先训练的语言模型研究知识接地的对话生成。为了利用在容量约束下的冗余外部知识，我们提出了由预训练的语言模型与知识选择模块定义的响应生成，以及无监督的方法，以共同优化知识选择和通过未标记的对话来优化知识选择和响应。两个基准的经验结果表明，在自动评估和人类判断中，我们的模型可以显着胜过最先进的方法。
We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues. Empirical results on two benchmarks indicate that our model can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
