Successful conversations often rest on common understanding, where all parties are on the same page about the information being shared. This process, known as conversational grounding, is crucial for building trustworthy dialog systems that can accurately keep track of and recall the shared information. The proficiencies of an agent in grounding the conveyed information significantly contribute to building a reliable dialog system. Despite recent advancements in dialog systems, there exists a noticeable deficit in their grounding capabilities. Traum provided a framework for conversational grounding introducing Grounding Acts and Grounding Units, but substantial progress, especially in the realm of Large Language Models, remains lacking. To bridge this gap, we present the annotation of two dialog corpora employing Grounding Acts, Grounding Units, and a measure of their degree of grounding. We discuss our key findings during the annotation and also provide a baseline model to test the performance of current Language Models in categorizing the grounding acts of the dialogs. Our work aims to provide a useful resource for further research in making conversations with machines better understood and more reliable in natural day-to-day collaborative dialogs.

翻译：成功对话往往建立于共同理解之上，即所有参与者对所共享信息达成一致。这一过程被称为"会话接地"，对于构建能够准确追踪和回忆共享信息的可信对话系统至关重要。智能体在传递信息时的接地能力对构建可靠对话系统具有显著贡献。尽管对话系统近期取得进展，但其接地能力仍存在明显不足。Traum提出了引入接地行为与接地单元的会话接地框架，但尤其在大型语言模型领域尚未取得实质性突破。为弥补这一空白，我们采用接地行为、接地单元及其接地程度度量方法，对两个对话语料库进行了标注。本文讨论了标注过程中的关键发现，并提供了基线模型以测试当前语言模型对对话接地行为的分类能力。本研究成果旨在为提升人机对话在自然日常协作场景中的理解度与可靠性提供实用资源。