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.

此研究提供了通过使用对话中的对接行为和对接单元进行标注的两个对话语料库及其对接程度的度量，以测试当前的语言模型在对话的对接行为分类中的性能，并旨在进一步研究使与机器的对话更易理解和更可靠的资源。