Go to ACL ARR 2026 January homepageTRACE: A Corpus of Team Creative Discussions
Keywords: team creativity, collaborative discourse, multi-party dialogue, discussion dynamics
Abstract: Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale. We introduce \textsc{Trace}, a corpus of 309 group discussions from 103 teams (460 participants) across six creative problem-solving tasks. The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes. Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions: \textbf{Coherence}, \textbf{Exploration}, \textbf{Convergence}, and \textbf{Participation}. Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration. Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content. Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness. These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems.
Paper Type: Long
Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good
Research Area Keywords: corpus creation, human behavior analysis, conversation, NLP datasets
Contribution Types: Data resources, Data analysis
Languages Studied: English
Submission Number: 2351
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