DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving
Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured, reasoning-intensive tasks. We introduce DeliChess, a dataset of group deliberation dialogues in which participants collaboratively solve multiple-choice chess puzzles. Participants first answer independently, then engage in multi-party deliberation and revise their individual answers. The dataset comprises 107 dialogues with full transcripts, pre- and post-deliberation choices, and utterance-level annotations of communicative function, epistemic stance, and usefulness for supporting deliberation. Our analyses show that greater diversity in ...
| Authors | Xiaochen Zhu, Georgi Karadzhov, Tom Stafford et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 1 August 2026 |
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