中文标题#
通过领域提示和并行注意进行对话中的可推广参与度估计
英文标题#
Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention
中文摘要#
准确的参与度估计对于自适应人机交互系统至关重要，然而在不同领域中的鲁棒部署受到跨领域泛化能力差和建模复杂交互动态挑战的阻碍。为解决这些问题，我们提出了 DAPA（领域自适应并行注意力），一种用于可泛化对话参与度建模的新框架。 DAPA 通过在输入前添加可学习的领域特定向量引入领域提示机制，明确地将模型条件设置为数据来源，以促进领域感知的适应，同时保持可泛化的参与度表示。 为了捕捉交互同步性，该框架还集成了一个并行交叉注意力模块，该模块显式地对齐参与者之间的反应状态（前向 BiLSTM）和预期状态（后向 BiLSTM）。大量实验表明，DAPA 在多个跨文化及跨语言基准上建立了新的最先进性能，特别是在 NoXi-J 测试集上，相对于强基线模型，其一致性相关系数（CCC）绝对提升了 0.45。 我们的方法优势也通过在 MultiMediate'25 多领域参与度估计挑战赛中获得第一名得到了证实。
英文摘要#
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.
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