  Generative models have demonstrated impressive results on Aspect-based
Sentiment Analysis (ABSA) tasks, particularly for the emerging task of
extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these
models struggle with implicit sentiment expressions, which are commonly
observed in opinionated content such as online reviews. In this work, we
introduce GEN-SCL-NAT, which consists of two techniques for improved structured
generation for ACOS quadruple extraction. First, we propose GEN-SCL, a
supervised contrastive learning objective that aids quadruple prediction by
encouraging the model to produce input representations that are discriminable
across key input attributes, such as sentiment polarity and the existence of
implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured
generation format that better adapts autoregressive encoder-decoder models to
extract quadruples in a generative fashion. Experimental results show that
GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging
1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset.
Additionally, we see significant gains on implicit aspect and opinion splits
that have been shown as challenging for existing ACOS approaches.
