论文标题
恩典：基于方面的情感分析的梯度协调和级联标签
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
论文作者
论文摘要
在本文中，我们关注的是不平衡问题，在方面提取和方面情感分类中很少研究它们作为序列标记任务。此外，以前的作品通常会忽略标记极性时方面术语之间的相互作用。我们提出了一个梯度统一和级联的标签模型（GRACE）来解决这些问题。具体而言，开发了一个级联的标签模块，以增强方面术语之间的互换并在标记情感极性时提高情感令牌的注意力。极性序列旨在取决于生成的方面术语标签。为了减轻不平衡问题，我们通过动态调整每个标签的重量，将用于对象检测的梯度统一机制扩展到基于方面的情感分析。拟议中的恩典采用后后的伯特作为骨干。实验结果表明，所提出的模型可以在多个基准数据集上提高一致性，并生成最先进的结果。
In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities. We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. The polarities sequence is designed to depend on the generated aspect terms labels. To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone. Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.
