Knowledge-driven dialog system has recently made remarkable breakthroughs. Compared with general dialog systems, superior knowledge-driven dialog systems can generate more informative and knowledgeable responses with pre-provided knowledge. However, in practical applications, the dialog system cannot be provided with corresponding knowledge in advance because it cannot know in advance the development of the conversation. Therefore, in order to make the knowledge dialogue system more practical, it is vital to find a way to retrieve relevant knowledge based on the dialogue history. To solve this problem, we design a knowledge-driven dialog system named DRKQG (Dynamically Retrieving Knowledge via Query Generation for informative dialog response). Specifically, the system can be divided into two modules: the query generation module and the dialog generation module. First, a time-aware mechanism is utilized to capture context information, and a query can be generated for retrieving knowledge through search engine. Then, we integrate the copy mechanism and transformers, which allows the response generation module to produce responses derived from the context and retrieved knowledge. Experimental results at LIC2022, Language and Intelligence Technology Competition, show that our module outperforms the baseline model by a large margin on automatic evaluation metrics, while human evaluation by the Baidu Linguistics team shows that our system achieves impressive results in Factually Correct and Knowledgeable.

翻译：知识驱动的对话系统近期取得了显著突破。与通用对话系统相比，优质的知识驱动对话系统能够利用预提供的知识生成更丰富、更具知识性的回应。然而在实际应用中，对话系统无法预先获知对话的发展方向，因此难以提前提供相应的知识。为使知识对话系统更具实用性，基于对话历史检索相关知识的方法至关重要。为解决此问题，我们设计了一种名为DRKQG（动态查询生成以检索知识的知性对话响应）的知识驱动对话系统。该系统具体包含两个模块：查询生成模块与对话生成模块。首先，采用时间感知机制捕捉上下文信息，通过搜索引擎生成用于知识检索的查询；其次，结合复制机制与Transformer架构，使响应生成模块能够基于上下文与检索知识生成回复。在LIC2022语言与智能技术竞赛中的实验结果表明，自动评估指标下我们的模块以显著优势超越基线模型，而百度语言学团队的人工评估则显示，我们的系统在事实正确性与知识丰富性方面取得了出色效果。