利用知识图谱通过接地交流弥合对话中的信息鸿沟
Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs
AI总结：
本文构建五领域对话语料 BridgeKG，并基于知识图谱评估大型语言模型的接地行为分类与信息项识别能力。
摘要：
知识模型是对话系统实现会话交互的基础，而此类交互需要处理特定领域知识。要确保信息提供型对话中的有效沟通，就必须使用户理解与系统可获得的知识保持一致。然而，自然语言中信息的表达方式，与系统内部知识中信息的表示方式之间存在语义不一致，对话系统因此经常面临挑战。为解决这一问题，我们研究了大型语言模型在会话接地方面的潜力；会话接地是一种通过在对话参与者之间建立共享知识来弥合信息鸿沟的机制。我们的方法包括对五个知识领域中的人类对话进行标注，以创建一个名为 BridgeKG 的新对话语料库。通过在该数据集上开展一系列实验，我们实证评估了大型语言模型在知识图谱结构内分类接地行为并识别已接地信息项的能力。我们的发现揭示了这些模型如何将会话内学习用于会话接地任务，以及常见预测错误；我们用具有挑战性的对话示例对此进行说明。我们还讨论了模型如何将知识图谱作为非结构化对话话语与结构化信息项之间的语义层来处理。
英文摘要：
Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in information-providing conversations entails aligning user understanding with the knowledge available to the system. However, dialogue systems often face challenges arising from semantic inconsistencies in how information is expressed in natural language compared to how it is represented within the system's internal knowledge. To address this problem, we study the potential of large language models for conversational grounding, a mechanism to bridge information gaps by establishing shared knowledge between dialogue participants. Our approach involves annotating human conversations across five knowledge domains to create a new dialogue corpus called BridgeKG. Through a series of experiments on this dataset, we empirically evaluate the capabilities of large language models in classifying grounding acts and identifying grounded information items within a knowledge graph structure. Our findings offer insights into how these models use in-context learning for conversational grounding tasks and common prediction errors, which we illustrate with examples from challenging dialogues. We discuss how the models handle knowledge graphs as a semantic layer between unstructured dialogue utterances and structured information items.
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。
