In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues

Understanding the structure of multi-party conversation and the intentions and dialogue acts of each speaker remains a significant challenge in NLP. While a number of corpora annotated using theoretical frameworks of dialogue have been proposed, these typically focus on either utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue, losing fine-grained intents captured in dialogue acts. Recently, the Dependency Dialogue Acts (DDA) framework has been proposed for modeling both the fine-grained intents of each speaker and the structure of multi-party dialogues (Cai et al., 2023) . However, there is not yet a corpus annotated with this framework available for the community to study. To address this gap, we introduce a new corpus of 33 English language dialogues with over 9,000 utterance units, densely annotated using the Dependency Dialogue Acts (DDA) framework.Our dataset spans four genres of multi-party conversations from different modalities: (1) physics classroom discussions, (2) engineering classroom discussions, (3) board game interactions, and (4) written online game chat logs. Each session is doubly annotated and adjudicated to ensure high-quality labeling. We present a description of the dataset and annotation process, an analysis of speaker dynamics enabled by our annotation, and a baseline evaluation of LLMs as DDA parsers. We discuss the implications of this dataset for understanding dynamics between speakers and for developing more controllable dialogue agents.