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.

本文探讨了模板顺序对方面情感四元组预测任务的影响，并提出了通过最小化熵选择模板顺序并使用多种合适的模板进行数据增强提高该任务表现的方法。在低资源情况下，该方法优于现有的最先进方法。

通过模板 - 顺序数据增强改进方面情感四元组预测

Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation

Aspect-level sentiment analysis aims to determine the sentiment polarity
towards a specific target in a sentence. The main challenge of this task is to
effectively model the relation between targets and sentiments so as to filter
out noisy opinion words from irrelevant targets. Most recent efforts capture
relations through target-sentiment pairs or opinion spans from a word-level or
phrase-level perspective. Based on the observation that targets and sentiments
essentially establish relations following the grammatical hierarchy of
phrase-clause-sentence structure, it is hopeful to exploit comprehensive
syntactic information for better guiding the learning process. Therefore, we
introduce the concept of Scope, which outlines a structural text region related
to a specific target. To jointly learn structural Scope and predict the
sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to
synthesize information from constituency tree and dependency tree, exploring
the potential of linking two syntax parsing methods to enrich the
representation. Experimental results on four public datasets illustrate that
our HGCN model outperforms current state-of-the-art baselines.

提出了 Scope 的概念，用于描述与特定目标相关的结构文本区域。结合组成树和依赖树的信息，提出了一个杂交图卷积网络（HGCN）来共同学习结构 Scope 并预测情感极性，证明该模型优于当前最先进的基线模型。