Aspect-based sentiment analysis (ABSA), a nuanced task in text analysis,
seeks to discern sentiment orientation linked to specific aspect terms in text.
Traditional approaches often overlook or inadequately model the explicit
syntactic structures of sentences, crucial for effective aspect term
identification and sentiment determination. Addressing this gap, we introduce
an innovative model: Syntactic Dependency Enhanced Multi-Task Interaction
Architecture (SDEMTIA) for comprehensive ABSA. Our approach innovatively
exploits syntactic knowledge (dependency relations and types) using a
specialized Syntactic Dependency Embedded Interactive Network (SDEIN). We also
incorporate a novel and efficient message-passing mechanism within a multi-task
learning framework to bolster learning efficacy. Our extensive experiments on
benchmark datasets showcase our model's superiority, significantly surpassing
existing methods. Additionally, incorporating BERT as an auxiliary feature
extractor further enhances our model's performance.

我们引入了一种创新模型：句法依赖增强的多任务交互架构 (SDEMTIA)，用于全面的基于方面的情感分析 (ABSA)。我们的方法创新地利用了句法知识 (依赖关系和类型)，并使用专门的句法依赖嵌入交互网络 (SDEIN)。我们还在多任务学习框架内结合了一种新颖高效的信息传递机制，以增强学习效果。我们在基准数据集上的广泛实验证明了我们模型的优越性，明显超越了现有方法。此外，引入 BERT 作为辅助特征提取器进一步提高了我们模型的性能。