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    <abstract>Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspectbased 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 aspectopinion-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.</abstract>
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%0 Conference Proceedings
%T A Unified One-Step Solution for Aspect Sentiment Quad Prediction
%A Zhou, Junxian
%A Yang, Haiqin
%A He, Yuxuan
%A Mou, Hao
%A Yang, JunBo
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-unified-one
%X Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspectbased 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 aspectopinion-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.
%R 10.18653/v1/2023.findings-acl.777
%U https://aclanthology.org/2023.findings-acl.777
%U https://doi.org/10.18653/v1/2023.findings-acl.777
%P 12249-12265

Markdown (Informal)

[A Unified One-Step Solution for Aspect Sentiment Quad Prediction](https://aclanthology.org/2023.findings-acl.777) (Zhou et al., Findings 2023)

• A Unified One-Step Solution for Aspect Sentiment Quad Prediction (Zhou et al., Findings 2023)

ACL

• Junxian Zhou, Haiqin Yang, Yuxuan He, Hao Mou, and JunBo Yang. 2023. A Unified One-Step Solution for Aspect Sentiment Quad Prediction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12249–12265, Toronto, Canada. Association for Computational Linguistics.