Abstract
I propose a novel dual-attention model(DAM) for aspect-level sentiment classification. Many methods have been proposed, such as support vector machines for artificial design features, long short-term memory networks based on attention mechanisms, and graph neural networks based on dependency parsing. While these methods all have decent performance, I think they all miss one important piece of syntactic information: dependency labels. Based on this idea, this paper proposes a model using dependency labels for the attention mechanism to do this task. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the dual attention model has good performance on all three datasets.
Abstract (translated)
我提出了一种新的多关注模型(DAM),用于 aspect-level 情感分类。已经提出了许多方法,如支持向量机用于人工设计特征、基于注意力机制的长期短期记忆网络,以及基于依赖解析的图神经网络。虽然这些方法都取得了不错的性能,但我觉得它们都缺少一个重要的语法信息:依赖标签。基于这个想法,本文提出了一种使用依赖标签的注意力机制模型来完成这个任务。我们评估了三种数据集:笔记本电脑和餐厅来自SemEval 2014,最后一个数据集是推特数据集。实验结果表明,双关注模型在所有三个数据集上取得了良好的性能。
URL
https://arxiv.org/abs/2303.07689
