Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task at fine-grained level. Recently, generative frameworks have attracted increasing attention in ABSA due to their ability to unify subtasks and their continuity to upstream pre-training tasks. However, these generative models suffer from the neighboring dependency problem that induces neighboring words to get higher attention. In this paper, we propose SynGen, a plug-and-play syntactic information aware module. As a plug-in module, our SynGen can be easily applied to any generative framework backbones. The key insight of our module is to add syntactic inductive bias to attention assignment and thus direct attention to the correct target words. To the best of our knowledge, we are the first one to introduce syntactic information to generative ABSA frameworks. Our module design is based on two main principles: (1) maintaining the structural integrity of backbone PLMs and (2) disentangling the added syntactic information and original semantic information. Empirical results on four popular ABSA datasets demonstrate that SynGen enhanced model achieves a comparable performance to the state-of-the-art model with relaxed labeling specification and less training consumption.

翻译：方面级情感分析（ABSA）是一种细粒度的情感分析任务。近年来，生成式框架因能够统一子任务且与上游预训练任务保持连续性，在ABSA领域受到越来越多的关注。然而，这类生成模型存在相邻依赖问题，导致邻近词获得更高的注意力权重。本文提出SynGen——一种即插即用的句法信息感知模块。作为插件模块，我们的SynGen可轻松应用于任意生成式框架主干。该模块的核心思路是在注意力分配中加入句法归纳偏置，从而将注意力引导至正确的目标词。据我们所知，这是首次将句法信息引入生成式ABSA框架。模块设计基于两大原则：（1）保持主干预训练语言模型（PLM）的结构完整性；（2）解耦所添加的句法信息与原始语义信息。在四个主流ABSA数据集上的实验结果表明，经SynGen增强的模型在放宽标注规范且训练消耗更低的条件下，取得了与当前最优模型相当的性能。