Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus

TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialogues

We present TRACE, a novel system for live *common ground* tracking in situated collaborative tasks. With a focus on fast, real-time performance, TRACE tracks the speech, actions, gestures, and visual attention of partici…

The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents

Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learning

The field of learning analytics has made notable strides in automating the detection of complex learning processes in multimodal data. However, most advancements have focused on individualized problem-solving instead of …

Collaborate, Deliberate, Evaluate: How LLM Alignment Affects Coordinated Multi-Agent Outcomes

As Large Language Models (LLMs) get integrated into diverse workflows, they are increasingly being regarded as "collaborators" with humans, and required to work in coordination with other AI systems. If such AI collabora…

Box of Lies: Multimodal Deception Detection in Dialogues

Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimod…