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Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task atfine-grained level. Recently, generative frameworks have attracted increasingattention in ABSA due to their ability to unify subtasks and their continuityto upstream pre-training tasks. However, these generative models suffer fromthe neighboring dependency problem that induces neighboring words to get higherattention. In this paper, we propose SynGen, a plug-and-play syntacticinformation aware module. As a plug-in module, our SynGen can be easily appliedto any generative framework backbones. The key insight of our module is to addsyntactic inductive bias to attention assignment and thus direct attention tothe correct target words. To the best of our knowledge, we are the first one tointroduce syntactic information to generative ABSA frameworks. Our moduledesign is based on two main principles: (1) maintaining the structuralintegrity of backbone PLMs and (2) disentangling the added syntacticinformation and original semantic information. Empirical results on fourpopular ABSA datasets demonstrate that SynGen enhanced model achieves acomparable performance to the state-of-the-art model with relaxed labelingspecification and less training consumption.

SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis