  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.

Computation and Language;Artificial Intelligence