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:与一般对话系统相比, 高级知识驱动的对话系统可以产生更丰富和知识化的响应。 然而, 在实际应用中, 对话系统无法事先提供相应的知识, 因为它无法事先知道对话的开发。 因此, 为了让知识对话系统更加实用, 必须找到一种方法, 以对话历史为基础检索相关知识。 为了解决这个问题, 我们设计了一个知识驱动的对话系统, 名为 DRKQG( 通过询问生成获取知识, 以提供信息性对话响应 ) 。 具体地说, 该系统可以分为两个模块: 查询生成模块和对话框生成模块。 首先, 利用时间认知机制来捕捉背景信息, 可以生成查询, 通过搜索引擎重新获取知识。 然后, 我们整合复制机制和变异器, 使响应生成模块能够产生来自上下文和检索知识的反应。 在 LIC.2022 语言和情报技术竞争中的实验结果, 显示我们的模块超越了基线模型, 并且通过一个大型的日历评估团队, 通过一个可理解的日历模型, 通过一个巨大的日历模型, 通过一个巨大的自动评估团队, 来显示一个可理解的模型。