arXiv:2606.00012v1 Announce Type: new Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies are mostly limited to textual modality or two-party dialogue, failing to meet the multimodal and multi-party settings. In this paper, we construct the first publicly available English multimodal dataset DraDDP for multi-party dialogue discourse parsing, based on American TV dramas. DraDDP contains 495 dialogue segments with 6,374 utterances and 9.1 hours of parallel video content, covering rich mul
The proliferation of multimodal data and the drive for more sophisticated AI understanding of real-world interactions are accelerating the need for such datasets.
This new dataset provides a critical foundation for developing more advanced AI models capable of understanding complex human communication, crucial for AI agents and natural interaction.
The availability of DraDDP enables the training of AI models that can parse multimodal, multi-party dialogues, moving beyond text-only or two-party limitations, thus enhancing the capability of conversational AI.
- · AI researchers and developers
- · Companies developing AI agents
- · Natural Language Processing (NLP) sector
- · Generative AI platforms
- · AI models reliant solely on unimodal data
- · Legacy dialogue parsing methods
Improved multimodal dialogue systems with better context and intent understanding will emerge.
More robust and human-like AI agents capable of participating in and understanding complex group conversations will become feasible.
Enhanced AI-driven interaction could lead to new forms of collaborative work involving AI and humans, potentially impacting white-collar workflows significantly.
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