@inproceedings{chen-etal-2025-caca,
title = "{CACA}: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction",
author = "Chen, Bingfeng and
Xu, Haoran and
Luo, Yongqi and
Xu, Boyan and
Cai, Ruichu and
Hao, Zhifeng",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.635/",
pages = "9472--9484",
abstract = "Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with \textit{Context-Aware Cross-Attention Network}. When implicit terms are present, the \textit{Context-Aware Cross-Attention Network} enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at \url{https://github.com/DMIRLAB-Group/CACA}."
}
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<abstract>Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network. When implicit terms are present, the Context-Aware Cross-Attention Network enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at https://github.com/DMIRLAB-Group/CACA.</abstract>
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%0 Conference Proceedings
%T CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction
%A Chen, Bingfeng
%A Xu, Haoran
%A Luo, Yongqi
%A Xu, Boyan
%A Cai, Ruichu
%A Hao, Zhifeng
%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 chen-etal-2025-caca
%X Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network. When implicit terms are present, the Context-Aware Cross-Attention Network enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at https://github.com/DMIRLAB-Group/CACA.
%U https://aclanthology.org/2025.coling-main.635/
%P 9472-9484
Markdown (Informal)
[CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction](https://aclanthology.org/2025.coling-main.635/) (Chen et al., COLING 2025)
ACL