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.

这篇论文提出了一种新颖的动态多尺度上下文聚合网络（DMCA）来解决对话中的目标 - 方面 - 意见 - 情感的提取过程中遇到的挑战。实验结果表明，DMCA 模型明显优于基线模型，达到了最先进的性能。