Conversational Aspect-Based Sentiment Analysis (DiaASQ) aims to detect quadruples \{target, aspect, opinion, sentiment polarity\} from given dialogues. In DiaASQ, elements constituting these quadruples are not necessarily confined to individual sentences but may span across multiple utterances within a dialogue. This necessitates a dual focus on both the syntactic information of individual utterances and the semantic interaction among them. However, previous studies have primarily focused on coarse-grained relationships between utterances, thus overlooking the potential benefits of detailed intra-utterance syntactic information and the granularity of inter-utterance relationships. This paper introduces the Triple GNNs network to enhance DiaAsQ. It employs a Graph Convolutional Network (GCN) for modeling syntactic dependencies within utterances and a Dual Graph Attention Network (DualGATs) to construct interactions between utterances. Experiments on two standard datasets reveal that our model significantly outperforms state-of-the-art baselines. The code is available at \url{https://github.com/nlperi2b/Triple-GNNs-}.

翻译：会话方面级情感分析（DiaASQ）旨在从给定对话中检测四元组{目标、方面、观点、情感极性}。在DiaASQ中，构成这些四元组的元素不一定局限于单个句子，而是可能跨越对话中的多个话语。这要求同时关注单个话语的句法信息以及它们之间的语义交互。然而，先前研究主要关注话语间的粗粒度关系，从而忽略了细粒度的话语内部句法信息以及话语间关系的层次性可能带来的潜在优势。本文引入三重图神经网络（Triple GNNs）以增强DiaASQ。该网络利用图卷积网络（GCN）建模话语内部的句法依赖关系，并采用双图注意力网络（DualGATs）构建话语间的交互。在两个标准数据集上的实验表明，我们的模型显著优于当前最先进的基线方法。代码可在 \url{https://github.com/nlperi2b/Triple-GNNs-} 获取。