Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with
  Extract-Then-Assign Strategy
Jieyong Kim Ryang Heo Yongsik Seo SeongKu Kang Jinyoung Yeo Dongha Lee
In the task of aspect sentiment quad prediction (ASQP), generative methods
for predicting sentiment quads have shown promising results. However, they
still suffer from imprecise predictions and limited interpretability, caused by
data scarcity and inadequate modeling of the quadruplet composition process. In
this paper, we propose Self-Consistent Reasoning-based Aspect-sentiment
quadruple Prediction (SCRAP), optimizing its model to generate reasonings and
the corresponding sentiment quadruplets in sequence. SCRAP adopts the
Extract-Then-Assign reasoning strategy, which closely mimics human cognition.
In the end, SCRAP significantly improves the model's ability to handle complex
reasoning tasks and correctly predict quadruplets through consistency voting,
resulting in enhanced interpretability and accuracy in ASQP.