Knowledge-based dialogue systems with internet retrieval have recently attracted considerable attention from researchers. The dialogue systems overcome a major limitation of traditional knowledge dialogue systems, where the timeliness of knowledge cannot be assured, hence providing greater practical application value. Knowledge-based dialogue systems with internet retrieval can be typically segmented into three tasks: Retrieval Decision, Query Generation, and Response Generation. However, many of studies assumed that all conversations require external knowledge to continue, neglecting the critical step of determining when retrieval is necessary. This assumption often leads to an over-dependence on external knowledge, even when it may not be required. Our work addresses this oversight by employing a single unified model facilitated by prompt and multi-task learning approaches. This model not only decides whether retrieval is necessary but also generates retrieval queries and responses. By integrating these functions, our system leverages the full potential of pre-trained models and reduces the complexity and costs associated with deploying multiple models. We conducted extensive experiments to investigate the mutual enhancement among the three tasks in our system. What is more, the experiment results on the Wizint and Dusinc datasets not only demonstrate that our unified model surpasses the baseline performance for individual tasks, but also reveal that it achieves comparable results when contrasted with SOTA systems that deploy separate, specialized models for each task.

翻译：基于互联网检索的知识对话系统近期引起了研究者的广泛关注。该类对话系统克服了传统知识对话系统中知识时效性无法保证的主要局限，因而具有更高的实际应用价值。基于互联网检索的知识对话系统通常可分解为三项任务：检索决策、查询生成与回复生成。然而，许多研究假设所有对话均需借助外部知识才能继续，忽略了判断何时需要检索这一关键步骤。该假设常导致对外部知识的过度依赖，即便在无需检索的情况下也不例外。本研究通过采用提示学习与多任务学习方法驱动的统一模型，弥补了这一疏漏。该模型不仅能够判断是否需要检索，还能同时生成检索查询与回复。通过整合这些功能，本系统充分发挥了预训练模型的潜力，并降低了部署多个模型带来的复杂性与成本。我们开展了大量实验以探究三项任务在本系统中的相互增强关系。此外，基于Wizint与Dusinc数据集的实验结果表明，本统一模型不仅在各单项任务上超越了基线性能，并且在每项任务均部署独立专用模型的最优系统对比中达到了可相媲美的结果。