  In this paper, we propose to enhance the pair-wise aspect and opinion terms
extraction (PAOTE) task by incorporating rich syntactic knowledge. We first
build a syntax fusion encoder for encoding syntactic features, including a
label-aware graph convolutional network (LAGCN) for modeling the dependency
edges and labels, as well as the POS tags unifiedly, and a local-attention
module encoding POS tags for better term boundary detection. During pairing, we
then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term
pairing, in the meantime re-harnessing the syntax-enriched representations in
LAGCN for syntactic-aware scoring. Experimental results on four benchmark
datasets demonstrate that our model outperforms current state-of-the-art
baselines, meanwhile yielding explainable predictions with syntactic knowledge.
