基于知识的社会机器人和会话代理的对话流管理
Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents
摘要：
本文提出了一种基于知识的会话系统，为社会机器人和其他会话代理设计。所提出的系统依赖于一个本体的所有概念，可能是相关的会话主题，以及它们的相互关系的描述。本文重点介绍了对话管理的算法，该算法根据用户的输入选择最合适的对话主题。此外，它讨论了策略，以确保会话流捕捉，尽可能连贯地，用户的意图，以推动对话在特定的方向，同时避免纯粹的反应，用户说什么。为了衡量对话的质量，本文报告了对100名招募的参与者进行的测试，比较了五个对话代理：（i）仅基于语音中的关键字的检测来解决对话流管理的代理，（ii）基于关键字的检测和Google Cloud自然语言的内容分类特征两者的代理，（iii）一个随机选择对话主题的代理，（iv）一个假装是聊天机器人的人，以及（v）世界上最著名的聊天机器人之一：dika。参与者的主观感知通过SASSI（语音系统接口主观评估）工具以及用于测量连贯性主观感知的自定义调查来测量。
英文摘要：
The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
