The 2026 ACII Dyadic Conversations (ACII-DaiKon) Workshop & Challenge introduces a benchmark for modeling interpersonal affect and social dynamics in dyadic conversations. Although conversational affect modeling has advanced rapidly, most benchmarks remain speaker-centric and underrepresent coupled, time-evolving processes between partners, including directional influence, conversational timing coordination, and rapport development. To address this gap, ACII-DaiKon presents three coordinated sub-challenges built on a shared dataset: (1) directional interpersonal influence prediction, (2) turn-taking prediction (next-speaker and time-to-next-speech), and (3) rapport trajectory prediction across full interactions. The challenge is built on the Hume-DaiKon dataset, comprising 945 dyadic conversations (743.4 hours of audiovisual data) collected under naturalistic conditions across five languages. The benchmark supports multimodal modeling, temporal reasoning, and cross-context generalization through fixed train/validation/test splits, standardized metrics, and released baseline systems. Evaluation uses Concordance Correlation Coefficient (CCC), Pearson correlation, Macro-F1, and Mean Absolute Error (MAE) depending on the sub-challenge. Baseline experiments establish initial reference performance, with best test results of 0.40 CCC and 0.50 Pearson for influence prediction, 0.66 Macro-F1 and 1.50~s MAE for turn-taking, and 0.68 CCC and 0.70 Pearson for rapport trajectory modeling. These results indicate that while current methods capture coarse dyadic patterns, robust modeling of directional dependence and long-horizon interpersonal dynamics remains challenging. The workshop provides a shared platform for rigorous comparison and cross-disciplinary discussion on data validity, evaluation protocols, and culturally aware modeling for dyadic interaction.

翻译：2026年ACII双人对话（ACII-DaiKon）研讨会与挑战赛引入了一个基准测试，用于建模双人对话中的人际情感与社交动态。尽管对话情感建模已取得快速进展，但现有基准多偏向说话人中心视角，未能充分表征对话伙伴间耦合的、随时间演化的过程，包括方向性影响、对话时机协调与融洽关系发展。为弥补这一不足，ACII-DaiKon基于共享数据集提出了三项协同子挑战：（1）方向性人际影响预测，（2）话轮转换预测（下一说话者及下一话轮时间间隔），以及（3）完整交互过程中的融洽轨迹预测。该挑战基于Hume-DaiKon数据集构建，该数据集包含945场双人对话（743.4小时视听数据），在自然条件下采集，涵盖五种语言。基准测试通过固定的训练/验证/测试集划分、标准化评估指标及公开的基线系统，支持多模态建模、时序推理及跨情境泛化。评估指标根据子挑战不同分别采用一致性相关系数（CCC）、皮尔逊相关系数、宏F1分数和平均绝对误差（MAE）。基线实验建立了初始参考性能，在影响预测任务中最佳测试结果达到CCC 0.40、皮尔逊相关系数0.50；话轮转换任务中宏F1分数0.66、MAE 1.50秒；融洽轨迹建模任务中CCC 0.68、皮尔逊相关系数0.70。这些结果表明，虽然现有方法能够捕捉粗略的双人对话模式，但对方向性依赖和长时程人际动态的稳健建模仍具挑战性。本研讨会为双人交互的数据有效性、评估协议及文化感知建模提供了严格的比较平台与跨学科讨论空间。