通过领域提示与并行注意力，提升对话中参与度估计的跨域泛化能力。
Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention
- 用可学习的领域向量显式引导模型，实现领域自适应
- 在跨文化测试集上提升0.45的CCC指标，超越强基线
- 适合需要跨场景对话分析的研究者与系统开发者
准确的参与度估计对自适应人机交互系统至关重要，但其鲁棒部署受限于跨领域泛化能力差及复杂交互动态建模困难。为此，我们提出DAPA（领域自适应并行注意力）框架，用于可泛化的对话参与度建模。DAPA引入领域提示机制，在输入前添加可学习的领域特定向量，显式将模型条件化于数据来源，以促进领域感知适应，同时保持可泛化的参与度表征。为捕捉互动同步性，框架还集成并行交叉注意力模块，显式对齐参与者之间的反应态（前向BiLSTM）与预期态（后向BiLSTM）。大量实验表明，DAPA在多个跨文化和跨语言基准上达到新最佳性能，尤其在NoXi-J测试集上相比强基线绝对提升0.45的一致相关系数（CCC）。该方法在MultiMediate'25多领域参与度估计挑战赛中获得第一名，验证了其优越性。
原文摘要 · Abstract (English)
Accurate engagement estimation is essential for adaptive human-computer interaction systems, yet robust deployment is hindered by poor generalizability across diverse domains and challenges in modeling complex interaction dynamics.To tackle these issues, we propose DAPA (Domain-Adaptive Parallel Attention), a novel framework for generalizable conversational engagement modeling. DAPA introduces a Domain Prompting mechanism by prepending learnable domain-specific vectors to the input, explicitly conditioning the model on the data's origin to facilitate domain-aware adaptation while preserving generalizable engagement representations. To capture interactional synchrony, the framework also incorporates a Parallel Cross-Attention module that explicitly aligns reactive (forward BiLSTM) and anticipatory (backward BiLSTM) states between participants.Extensive experiments demonstrate that DAPA establishes a new state-of-the-art performance on several cross-cultural and cross-linguistic benchmarks, notably achieving an absolute improvement of 0.45 in Concordance Correlation Coefficient (CCC) over a strong baseline on the NoXi-J test set. The superiority of our method was also confirmed by winning the first place in the Multi-Domain Engagement Estimation Challenge at MultiMediate'25.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。