Conversational aspect-based sentiment quadruple analysis (DiaASQ) aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue. In DiaASQ, a quadruple's elements often cross multiple utterances. This situation complicates the extraction process, emphasizing the need for an adequate understanding of conversational context and interactions. However, existing work independently encodes each utterance, thereby struggling to capture long-range conversational context and overlooking the deep inter-utterance dependencies. In this work, we propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges. Specifically, we first utilize dialogue structure to generate multi-scale utterance windows for capturing rich contextual information. After that, we design a Dynamic Hierarchical Aggregation module (DHA) to integrate progressive cues between them. In addition, we form a multi-stage loss strategy to improve model performance and generalization ability. Extensive experimental results show that the DMCA model outperforms baselines significantly and achieves state-of-the-art performance.

翻译：会话方面级情感四元组分析（DiaASQ）旨在提取对话中的目标-方面-观点-情感四元组。在DiaASQ中，一个四元组的元素往往跨越多个话语。这种情况使得提取过程复杂化，强调了对对话上下文和交互进行充分理解的重要性。然而，现有工作独立编码每个话语，因此难以捕获长距离的对话上下文，并忽略了深层的跨话语依赖性。在本工作中，我们提出了一种新颖的动态多尺度上下文聚合网络（DMCA）以应对这些挑战。具体而言，我们首先利用对话结构生成多尺度话语窗口以捕获丰富的上下文信息。之后，我们设计了一个动态层次聚合模块（DHA）来整合它们之间的渐进线索。此外，我们构建了一个多阶段损失策略以提升模型性能和泛化能力。大量实验结果表明，DMCA模型显著优于基线方法，并取得了最先进的性能。