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.

为了实现对话系统的智能回答，文中设计了 DRKQG，该系统能够根据上下文信息，在没有提前准备知识库的情况下通过搜索引擎检索相关知识，并利用生成模块同时考虑上下文信息和检索知识生成回答，实验结果表明，该模块在自动评估和人工评估两方面取得了优异的成绩。