Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level
sentiment analysis. Current ASQP datasets are characterized by their small size
and low quadruple density, which hinders technical development. To expand
capacity, we construct two large Chinese ASQP datasets crawled from multiple
online platforms. The datasets hold several significant characteristics: larger
size (each with 10,000+ samples) and rich aspect categories, more words per
sentence, and higher density than existing ASQP datasets. Moreover, we are the
first to evaluate the performance of Generative Pre-trained Transformer (GPT)
series models on ASQP and exhibit potential issues. The experiments with
state-of-the-art ASQP baselines underscore the need to explore additional
techniques to address ASQP, as well as the importance of further investigation
into methods to improve the performance of GPTs.

通过构建两个大型的中文 aspect sentiment quad prediction（ASQP）数据集，我们评估了 GPT 系列模型在 ASQP 上的性能，并展示了潜在的问题，同时强调了需要探索额外技术以解决 ASQP 以及改善 GPT 性能的重要性。

中国方面情感四元预测基准的实证研究

An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad  Prediction

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
https://www.github.com/Datastory-CN/ASQP-Datasets.

本文提出了一种统一的一步解决方案 One-ASQP，用于检测方面类别并同时识别方面 - 意见 - 情感 (AOS) 三元组，旨在扩展 Aspect sentiment quad prediction (ASQP) 的能力，并公开了两个新的 ASQP 数据集，以解决现有基准和数据集规模较小的问题。