Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to model the syntax-semantic relationships inherent in triplet elements. However, they have yet to fully tap into the vast potential of syntactic and semantic information within the ASTE task. In this work, we propose a \emph{Dual Encoder: Exploiting the potential of Syntactic and Semantic} model (D2E2S), which maximizes the syntactic and semantic relationships among words. Specifically, our model utilizes a dual-channel encoder with a BERT channel to capture semantic information, and an enhanced LSTM channel for comprehensive syntactic information capture. Subsequently, we introduce the heterogeneous feature interaction module to capture intricate interactions between dependency syntax and attention semantics, and to dynamically select vital nodes. We leverage the synergy of these modules to harness the significant potential of syntactic and semantic information in ASTE tasks. Testing on public benchmarks, our D2E2S model surpasses the current state-of-the-art(SOTA), demonstrating its effectiveness.

翻译：方面情感三元组抽取（ASTE）是细粒度情感分析中的新兴任务。近期研究采用图神经网络（GNN）建模三元组元素中固有的句法-语义关系，然而尚未充分挖掘ASTE任务中句法与语义信息的巨大潜力。本文提出"双编码器：挖掘句法与语义潜力"模型（D2E2S），最大化词语间的句法与语义关联。具体而言，模型采用双通道编码器：BERT通道捕获语义信息，增强型LSTM通道实现全面句法信息捕捉。随后引入异构特征交互模块，捕获依存句法与注意力语义间的复杂交互，并动态筛选关键节点。通过协同这些模块，充分释放ASTE任务中句法与语义信息的显著潜力。在公开基准测试中，我们的D2E2S模型超越当前最优水平（SOTA），验证了其有效性。