Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis
<|reference_start|>Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis: 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-}.<|reference_end|>
@article{li2024triple,
    title={Triple GNNs: Introducing Syntactic and Semantic Information for
  Conversational Aspect-Based Quadruple Sentiment Analysis},
    author={Binbin Li, Yuqing Li, Siyu Jia, Bingnan Ma, Yu Ding, Zisen Qi,
  Xingbang Tan, Menghan Guo, Shenghui Liu},
    journal={arXiv preprint arXiv:2403.10065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10065},
    primaryClass={cs.CL}
}