School of Computing, Gachon University, 1342 Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.
Sensors (Basel). 2023 Jan 6;23(2):685. doi: 10.3390/s23020685.
For a task-oriented dialogue system to provide appropriate answers to and services for users' questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most appropriate knowledge snippet from the knowledge base, where external unstructured knowledge is used to respond to user requests that cannot be solved by the internal knowledge addressed by the database or application programming interface. Therefore, this paper constructs a three-step knowledge-grounded task-oriented dialogue system with knowledge-seeking-turn detection, knowledge selection, and knowledge-grounded generation. In particular, we propose a hierarchical structure of domain-classification, entity-extraction, and snippet-ranking tasks by subdividing the knowledge selection step. Each task is performed through the pre-trained language model with advanced techniques to finally determine the knowledge snippet to be used to generate a response. Furthermore, the domain and entity information obtained because of the previous task is used as knowledge to reduce the search range of candidates, thereby improving the performance and efficiency of knowledge selection and proving it through experiments.
为了使面向任务的对话系统能够为用户的问题提供恰当的回答和服务，它必须能够利用与对话主题相关的知识。因此，系统应该能够从知识库中选择最合适的知识片段，其中外部非结构化知识用于响应用户请求，这些请求无法通过数据库或应用程序编程接口所涉及的内部知识来解决。因此，本文构建了一个具有知识检索轮检测、知识选择和知识生成的三步式基于知识的任务型对话系统。特别是，我们通过细分知识选择步骤，提出了一个领域分类、实体提取和片段排序任务的层次结构。每个任务都是通过预先训练的语言模型和高级技术来执行的，最终确定要使用的知识片段来生成响应。此外，由于前一个任务而获得的领域和实体信息被用作知识，以缩小候选者的搜索范围，从而提高知识选择的性能和效率，并通过实验证明了这一点。
