Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis
Binbin Li, Yuqing Li, Siyu Jia, Bingnan Ma, Yulong Ding, Zisen Qi, Xingbang Tan, Menghan Guo, Shenghui Liu
Abstract
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 stateof-the-art baselines. The code is available at https://github.com/ nlperi2b/Triple-GNNs-.