Triple GNNs: Introducing Syntactic and Semantic Information for
  Conversational Aspect-Based Quadruple Sentiment Analysis
Binbin Li Yuqing Li Siyu Jia Bingnan Ma Yu Ding Zisen Qi Xingbang Tan Menghan Guo Shenghui Liu
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-}.