Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy
This work addresses data scarcity and modeling challenges in ASQP, an incremental improvement for sentiment analysis tasks.
The paper tackles the problem of imprecise predictions and limited interpretability in aspect sentiment quad prediction (ASQP) by proposing SCRAP, a method that uses self-consistent reasoning and an extract-then-assign strategy, resulting in enhanced interpretability and accuracy.
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.