Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplet of an
aspect term, an opinion term, and their corresponding sentiment polarity from
the review texts. Due to the complexity of language and the existence of
multiple aspect terms and opinion terms in a single sentence, current models
often confuse the connections between an aspect term and the opinion term
describing it. To address this issue, we propose a pairing enhancement approach
for ASTE, which incorporates contrastive learning during the training stage to
inject aspect-opinion pairing knowledge into the triplet extraction model.
Experimental results demonstrate that our approach performs well on four ASTE
datasets (i.e., 14lap, 14res, 15res and 16res) compared to several related
classical and state-of-the-art triplet extraction methods. Moreover, ablation
studies conduct an analysis and verify the advantage of contrastive learning
over other pairing enhancement approaches.