A limitation of current neural dialog models is that they tend to suffer from
a lack of specificity and informativeness in generated responses, primarily due
to dependence on training data that covers a limited variety of scenarios and
conveys limited knowledge. One way to alleviate this issue is to extract
relevant knowledge from external sources at decoding time and incorporate it
into the dialog response. In this paper, we propose a post-hoc
knowledge-injection technique where we first retrieve a diverse set of relevant
knowledge snippets conditioned on both the dialog history and an initial
response from an existing dialog model. We construct multiple candidate
responses, individually injecting each retrieved snippet into the initial
response using a gradient-based decoding method, and then select the final
response with an unsupervised ranking step. Our experiments in goal-oriented
and knowledge-grounded dialog settings demonstrate that human annotators judge
the outputs from the proposed method to be more engaging and informative
compared to responses from prior dialog systems. We further show that
knowledge-augmentation promotes success in achieving conversational goals in
both experimental settings.

本文介绍了一种基于后处理的知识注入技术，该技术从外部源中提取相关知识，并将其合并到对话响应中，实验表明使用这种方法可以在目标导向和基于知识的对话环境中实现更具吸引力和更丰富的对话响应。