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        <title>Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction</title>
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    <abstract>Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction
%A Su, Guixin
%A Zhang, Yongcheng
%A Wang, Tongguan
%A Wu, Mingmin
%A Sha, Ying
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F su-etal-2025-unified
%X Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.
%U https://aclanthology.org/2025.coling-main.269/
%P 3997-4010

Markdown (Informal)

[Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction](https://aclanthology.org/2025.coling-main.269/) (Su et al., COLING 2025)

• Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (Su et al., COLING 2025)

ACL

• Guixin Su, Yongcheng Zhang, Tongguan Wang, Mingmin Wu, and Ying Sha. 2025. Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3997–4010, Abu Dhabi, UAE. Association for Computational Linguistics.