  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}.
