人机交互中基于图到文本方法的知识驱动响应生成
A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction
AI总结：
本文提出一种基于图到文本机制的人机交互对话模型，通过将持续更新的知识图谱转换为文本提示输入大语言模型生成响应，显著提升了机器人回答的事实性。
摘要：
知识图谱常用于以灵活高效的方式表示结构化信息，但其在情境对话中的应用仍待深入探索。本文提出了一种用于人机交互的新型对话模型，该模型基于对话状态的图表示。表示对话状态的知识图谱通过机器人传感器获取的新观测数据（包括语言、情境和多模态输入）进行持续更新，并由其他模块（特别是空间理解模块）进一步丰富。用于响应用户话语的神经对话模型依赖于一种简单但有效的图到文本机制，该机制遍历对话状态图并将遍历结果转换为自然语言形式。这种状态图到文本的转换使用一组参数化函数执行，而这些参数的值基于少量 Wizard-of-Oz 交互进行优化。转换完成后，对话状态图的文本表示作为提示词的一部分输入到大型语言模型中，用于解码智能体的响应。本研究通过一项用户研究对所提方法进行了实证评估，该研究使用一台充当对话伙伴的人形机器人，以评估图到文本机制对响应生成的影响。在机器人完成室内环境导览后，参与者通过口语对话与机器人交互，并评估机器人回答导览期间所观察问题的能力。用户评分显示，与使用语义三元组结构化输入的基线相比，采用图到文本方法时，机器人响应的感知事实性有统计学上的显著提升。
英文摘要：
Knowledge graphs are often used to represent structured information in a flexible and efficient manner, but their use in situated dialogue remains under-explored. This paper presents a novel conversational model for human--robot interaction that rests upon a graph-based representation of the dialogue state. The knowledge graph representing the dialogue state is continuously updated with new observations from the robot sensors, including linguistic, situated and multimodal inputs, and is further enriched by other modules, in particular for spatial understanding. The neural conversational model employed to respond to user utterances relies on a simple but effective graph-to-text mechanism that traverses the dialogue state graph and converts the traversals into a natural language form. This conversion of the state graph into text is performed using a set of parameterized functions, and the values for those parameters are optimized based on a small set of Wizard-of-Oz interactions. After this conversion, the text representation of the dialogue state graph is included as part of the prompt of a large language model used to decode the agent response. The proposed approach is empirically evaluated through a user study with a humanoid robot that acts as conversation partner to evaluate the impact of the graph-to-text mechanism on the response generation. After moving a robot along a tour of an indoor environment, participants interacted with the robot using spoken dialogue and evaluated how well the robot was able to answer questions about what the robot observed during the tour. User scores show a statistically significant improvement in the perceived factuality of the robot responses when the graph-to-text approach is employed, compared to a baseline using inputs structured as semantic triples.
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。
