  Recently, aspect sentiment quad prediction (ASQP) has become a popular task
in the field of aspect-level sentiment analysis. Previous work utilizes a
predefined template to paraphrase the original sentence into a structure target
sequence, which can be easily decoded as quadruplets of the form (aspect
category, aspect term, opinion term, sentiment polarity). The template involves
the four elements in a fixed order. However, we observe that this solution
contradicts with the order-free property of the ASQP task, since there is no
need to fix the template order as long as the quadruplet is extracted
correctly. Inspired by the observation, we study the effects of template orders
and find that some orders help the generative model achieve better performance.
It is hypothesized that different orders provide various views of the
quadruplet. Therefore, we propose a simple but effective method to identify the
most proper orders, and further combine multiple proper templates as data
augmentation to improve the ASQP task. Specifically, we use the pre-trained
language model to select the orders with minimal entropy. By fine-tuning the
pre-trained language model with these template orders, our approach improves
the performance of quad prediction, and outperforms state-of-the-art methods
significantly in low-resource settings.
