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

Dynamically Retrieving Knowledge via Query Generation for Informative Dialogue Generation