Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. The context, comprising utterances from previous dialogue exchanges, is used as a source of content for response generation and as a means of selecting external knowledge. However, to avoid introducing irrelevant content, it is key to enable fine-grained scoring of context and knowledge. In this paper, we present a novel approach to context and knowledge weighting as an integral part of model training. We guide the model training through a Contextual Knowledge Learning (CKL) process which involves Latent Vectors for context and knowledge, respectively. CKL Latent Vectors capture the relationship between context, knowledge, and responses through weak supervision and enable differential weighting of context utterances and knowledge sentences during the training process. Experiments with two standard datasets and human evaluation demonstrate that CKL leads to a significant improvement compared with the performance of six strong baseline models and shows robustness with regard to reduced sizes of training sets.

翻译：将对话上下文与知识融入对话生成模型对于提升生成回复质量至关重要。上下文由先前对话轮次中的话语组成，既作为回复生成的内容来源，也作为选择外部知识的手段。然而，为避免引入无关内容，关键在于实现上下文与知识的细粒度加权。本文提出了一种将上下文与知识加权作为模型训练组成部分的新方法。我们通过上下文知识学习（CKL）过程引导模型训练，该过程分别利用上下文和知识的潜在向量。CKL潜在向量通过弱监督捕获上下文、知识与回复之间的关系，并在训练过程中实现上下文话语与知识句子的差异化加权。基于两个标准数据集和人工评估的实验表明，与六个强基线模型相比，CKL显著提升了性能，并展现出对训练集规模缩减的鲁棒性。