Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, in this paper, we release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. With such datasets, we unveil the shortcomings of existing strong ASQP baselines and therefore propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspect-opinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds several unique advantages: (1) by separating ASQP into two subtasks and solving them independently and simultaneously, we can avoid error propagation in pipeline-based methods and overcome slow training and inference in generation-based methods; (2) by introducing sentiment-specific horns tagging schema in a token-pair-based two-dimensional matrix, we can exploit deeper interactions between sentiment elements and efficiently decode the AOS triplets; (3) we design ``[NULL]'' token can help us effectively identify the implicit aspects or opinions. Experiments on two benchmark datasets and our released two datasets demonstrate the advantages of our One-ASQP. The two new datasets are publicly released at \url{https://www.github.com/Datastory-CN/ASQP-Datasets}.

翻译：方面情感四元组预测（ASQP）是方面级情感分析中一项具有挑战性且重要的子任务，因为它提供了完整的方面级情感结构。然而，现有ASQP数据集通常规模小且密度低，阻碍了技术进展。为扩展能力，本文发布了两个用于ASQP的新数据集，其特点包括：更大规模、每个样本更多词数以及更高密度。基于这些数据集，我们揭示了现有强ASQP基线的缺陷，并提出一种名为One-ASQP的统一单步解决方案，用于同时检测方面类别并识别方面-观点-情感三元组（AOS三元组）。我们的One-ASQP具有以下独特优势：（1）通过将ASQP分离为两个子任务并独立且同步解决，可避免流水线方法中的错误传播，并克服生成方法中训练和推理缓慢的问题；（2）通过在基于词对二维矩阵中引入情感特定的标记标注方案，能够挖掘情感元素间的深层交互，并高效解码AOS三元组；（3）设计“[NULL]”标记可帮助有效识别隐式方面或观点。在两个基准数据集及我们发布的两个数据集上的实验证明了One-ASQP的优势。两个新数据集已在\url{https://www.github.com/Datastory-CN/ASQP-Datasets}公开。