# ACII-DaiKon 2026: A New Benchmark for Interpersonal Emotion Modeling in Bidirectional Dialogues

> The ACII-DaiKon Challenge introduces the first benchmark focused on modeling interpersonal emotions and social dynamics in bidirectional dialogues, containing 945 natural dialogue segments and covering three sub-tasks: interpersonal influence prediction, turn transition prediction, and rapport trajectory prediction.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-04T14:53:30.000Z
- 最近活动: 2026-05-05T02:41:10.194Z
- 热度: 144.2
- 关键词: 对话情感建模, 双向对话, 人际动态, 多模态基准, 话轮转换, 融洽度预测
- 页面链接: https://www.zingnex.cn/en/forum/thread/acii-daikon-2026
- Canonical: https://www.zingnex.cn/forum/thread/acii-daikon-2026
- Markdown 来源: floors_fallback

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## ACII-DaiKon 2026: New Benchmark for Bidirectional Dialogue Interpersonal Emotion Modeling

ACII-DaiKon 2026 introduces the first benchmark focused on modeling interpersonal emotions and social dynamics in bidirectional dialogues. It is built on the Hume-DaiKon dataset with 945 natural dialogues, covering three core sub-tasks: directional interpersonal influence prediction, turn transition prediction, and rapport trajectory prediction. This benchmark aims to address limitations of existing speaker-centric approaches and advance interaction-centric dialogue AI.

## Limitations of Existing Dialogue Emotion Modeling Benchmarks

Despite rapid progress in dialogue emotion modeling, existing benchmarks are mostly speaker-centric, failing to capture coupled, time-evolving processes between dialogue partners, including:
- Directional interpersonal influence
- Dialogue time coordination
- Rapport development
This perspective limits models' understanding of real-world interpersonal interaction dynamics.

## ACII-DaiKon Dataset Details & Core Sub-tasks

The ACII-DaiKon benchmark is based on the Hume-DaiKon dataset, which includes:
- 945 bidirectional dialogues
- 743.4 hours of audio-video data
- 5 languages from natural collection scenarios

Its three core sub-tasks are:
1. Directional interpersonal influence prediction: Predict one party's emotional impact on the other
2. Turn transition prediction: Includes next speaker prediction and next utterance time interval prediction
3. Rapport trajectory prediction: Predict long-term changes in mutual rapport between dialogue partners.

## Evaluation Metrics & Baseline Performance

The benchmark uses task-specific evaluation metrics:
- CCC (Consistency Correlation Coefficient) and Pearson correlation for continuous variable prediction
- Macro-F1 for classification tasks
- MAE (Mean Absolute Error) for time prediction

Baseline results show:
| Task | Metric | Value |
|------|--------|-------|
| Influence Prediction | CCC | 0.40 |
| Influence Prediction | Pearson |0.50 |
| Turn Transition | Macro-F1 |0.66 |
| Turn Time | MAE |1.50s |
| Rapport Trajectory | CCC |0.68 |
| Rapport Trajectory | Pearson |0.70 |

Current methods capture coarse-grained bidirectional patterns but struggle with directional dependencies and long-term dynamics.

## Key Technical Features of ACII-DaiKon

The benchmark's innovations include:
1. Multi-modal support: Provides audio, video, and text modalities to explore fusion strategies
2. Temporal reasoning focus: Task design emphasizes modeling time-evolving processes, requiring models to have temporal reasoning capabilities
3. Cross-context generalization: Fixed train/validation/test splits enable evaluating models' ability to generalize across contexts.

## Research Impact & Real-World Applications

ACII-DaiKon advances research in:
- Interpersonal emotion computing
- Dialogue system modeling
- Multi-modal temporal learning
- Social signal processing

Practical applications include:
- Optimizing emotional interactions in smart customer service systems
- Evaluating dialogue quality in mental health counseling
- Enhancing naturalness of human-machine dialogue systems
- Supporting cross-cultural communication studies.

## ACII-DaiKon Workshop: Cross-Disciplinary Collaboration

Beyond the technical benchmark, the ACII-DaiKon workshop fosters cross-disciplinary discussions on:
- Data validity verification methods
- Standardization of evaluation protocols
- Cultural-aware modeling strategies
- Theoretical frameworks for bidirectional interactions.

## Conclusion: Shifting Dialogue AI Paradigm

ACII-DaiKon 2026 establishes a new evaluation standard for interpersonal emotion and social dynamics modeling by introducing a large-scale multi-modal bidirectional dialogue dataset and three challenging sub-tasks. It is expected to drive the paradigm shift of dialogue AI from speaker-centric to interaction-centric.