论文标题
Alquist 3.0：使用对话知识图
Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
论文作者
论文摘要
在2020年Alexa奖竞赛中开发的第三个版本的开放域对话系统算法旨在就流行主题进行连贯而引人入胜的对话。主要的新颖贡献是引入一个系统，该系统利用基于会话知识图和邻接对的创新方法。对话知识图允许系统在对话中表达的知识，从而在对话中和对话中进行对话。对话邻接对对话将对话分为小的对话结构，可以将对话结合起来，并允许系统灵活地对广泛的用户输入做出反应。 我们讨论并描述了Alquist的管道，数据采集和处理，对话经理，NLG，知识汇总以及邻接对的层次结构。我们介绍了系统各个部分的实验结果。
The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics. The main novel contribution is the introduction of a system leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs. The conversational knowledge graph allows the system to utilize knowledge expressed during the dialogue in consequent turns and across conversations. Dialogue adjacency pairs divide the conversation into small conversational structures, which can be combined and allow the system to react to a wide range of user inputs flexibly. We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs. We present the experimental results of the individual parts of the system.
