  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.

Computation and Language