Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://github.com/KunPunCN/PT-GCN.

翻译：方面情感三元组抽取（ASTE）是一项新兴任务，旨在提取给定句子中的三元组，这些三元组由方面词、观点词和情感倾向组成。近期研究倾向于采用表格填充范式来处理该任务，其中词语关系被编码在二维表格中，且过程涉及明晰所有独立单元格以提取三元组。然而，这些研究忽略了相邻单元格之间的深层交互——我们发现这种交互对精确抽取十分有益。为此，我们针对ASTE任务提出了一种名为"基于提示的三通道图卷积神经网络"（PT-GCN）的新模型，该模型将关系表格转换为图结构，以探索更全面的关系信息。具体而言，我们将原始表格单元格视为节点，并利用提示注意力分数计算模块来确定边的权重。这使我们能够构建一个目标感知的网格状图，以增强整体抽取过程。随后，通过三通道卷积模块提取精确的情感知识。在基准数据集上的广泛实验表明，我们的模型达到了最先进的性能。代码已开源至 https://github.com/KunPunCN/PT-GCN。