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.

翻译：最近,情绪的反向预测(ASQP)在侧面情绪分析领域已成为一项受欢迎的任务。先前的工作使用一个预先定义的模板,将原句改写成结构目标序列,可以很容易地解码成形式上的四重形(类别、内容、见解、情绪极化)。模板涉及固定顺序的四个要素。然而,我们注意到,这一解决方案与ASQP任务无定序属性相矛盾,因为只要四重曲正确提取,就不需要固定模板顺序。根据观察,我们研究模板订单的效果,发现有些订单有助于基因化模型取得更好的性能。假设不同的订单提供四重体形形形形形形形形形形形形形色形形色形色色的各种不同观点。因此,我们提出了一个简单而有效的方法来确定最合适的订单,并进一步将多个合适的模板作为数据增强功能来改进ASQP任务。具体地说,我们使用预先培训的语言模式来选择最起码的版本。根据观察,通过微调校准前语言模型,我们用低重度的版本式式式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图式图