Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units
This work addresses the deficit in grounding capabilities for building reliable dialog systems, offering a resource for improving machine conversations in natural collaborative settings, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of conversational grounding in dialog systems by annotating two dialog corpora with Grounding Acts and Grounding Units, and provides a baseline model to evaluate current language models, achieving a performance benchmark for categorization tasks.
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.