Developing an efficient retriever to retrieve knowledge from a large-scale knowledge base (KB) is critical for task-oriented dialogue systems to effectively handle localized and specialized tasks. However, widely used generative models such as T5 and ChatGPT often struggle to differentiate subtle differences among the retrieved KB records when generating responses, resulting in suboptimal quality of generated responses. In this paper, we propose the application of maximal marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. In addition, our approach goes beyond considering solely retrieved entities and incorporates various meta knowledge to guide the generator, thus improving the utilization of knowledge. We evaluate our approach on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses. The codes and models of this paper are available at https://github.com/shenwzh3/MK-TOD.

翻译：在大型知识库（KB）中高效开发检索器以获取知识，对任务型对话系统有效处理本地化及专业化任务至关重要。然而，T5和ChatGPT等广泛使用的生成模型在生成响应时，往往难以区分检索到的知识库记录之间的细微差异，导致生成响应质量欠佳。本文提出应用最大边际似然法，通过利用响应生成过程中的信号来训练感知型检索器。此外，我们的方法不仅考虑检索实体，还融合多种元知识以指导生成器，从而提升知识利用率。我们以T5和ChatGPT作为骨干模型，在三个任务型对话数据集上评估了该方法。结果表明，结合元知识后，响应生成器能有效利用检索器提供的高质量知识记录，显著提升生成响应的质量。本文的代码与模型已公开于https://github.com/shenwzh3/MK-TOD。