中文标题#
透過領域提示和並行注意進行對話中的可推廣參與度估計
英文标题#
Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention
中文摘要#
準確的參與度估計對於自適應人機交互系統至關重要，然而在不同領域中的魯棒部署受到跨領域泛化能力差和建模複雜交互動態挑戰的阻礙。為解決這些問題，我們提出了 DAPA（領域自適應並行注意力），一種用於可泛化對話參與度建模的新框架。 DAPA 通過在輸入前添加可學習的領域特定向量引入領域提示機制，明確地將模型條件設置為數據來源，以促進領域感知的適應，同時保持可泛化的參與度表示。為了捕捉交互同步性，該框架還集成了一個並行交叉注意力模塊，該模塊明確地對齊參與者之間的反應狀態（前向 BiLSTM）和預期狀態（後向 BiLSTM）。大量實驗表明，DAPA 在多個跨文化及跨語言基準上建立了新的最先進性能，特別是在 NoXi-J 測試集上，相對於強基線模型，其一致性相關係數（CCC）絕對提升了 0.45。我們的方法優勢也通過在 MultiMediate'25 多領域參與度估計挑戰賽中獲得第一名得到了證實。
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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