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    <abstract>Aspect-based Sentiment Analysis (ABSA) is a fine-grained task. Recently, using graph convolutional networks (GCNs) to model syntactic information has become a popular topic. In addition, a growing consensus exists to enhance sentence representation using contrastive learning. However, when modeling syntactic information, incorrect syntactic structure may introduce additional noise. Meanwhile, we believe that contrastive learning implicitly introduce label information as priori. Therefore, we propose C³LPGCN, which integrates Contrastive Learning and Cooperative Learning with Prompt into GCN. Specifically, to alleviate the noise when modeling syntactic information, we propose mask-aware aspect information filter, which combines prompt information of template with aspect information to filter the syntactic information. Besides, we propose prompt-based contrastive learning and cooperative learning to utilise the label information further. On the one hand, we construct prompts containing labels for contrastive learning, by which the model can focus more on task-relevant features. On the other hand, cooperative learning further extracts label information by aligning input samples’ representation and output distribution with label samples. Extensive experiments on three datasets demonstrate that our method significantly improves the model’s performance compared to traditional contrastive learning methods. Moreover, our C³LPGCN outperforms state-of-the-art methods. Our source code and final models are publicly available at github</abstract>
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%0 Conference Proceedings
%T C³LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis
%A He, Ye
%A Zou, Shihao
%A YuzheChen, YuzheChen
%A Huang, Xianying
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F he-etal-2024-c3lpgcn
%X Aspect-based Sentiment Analysis (ABSA) is a fine-grained task. Recently, using graph convolutional networks (GCNs) to model syntactic information has become a popular topic. In addition, a growing consensus exists to enhance sentence representation using contrastive learning. However, when modeling syntactic information, incorrect syntactic structure may introduce additional noise. Meanwhile, we believe that contrastive learning implicitly introduce label information as priori. Therefore, we propose C³LPGCN, which integrates Contrastive Learning and Cooperative Learning with Prompt into GCN. Specifically, to alleviate the noise when modeling syntactic information, we propose mask-aware aspect information filter, which combines prompt information of template with aspect information to filter the syntactic information. Besides, we propose prompt-based contrastive learning and cooperative learning to utilise the label information further. On the one hand, we construct prompts containing labels for contrastive learning, by which the model can focus more on task-relevant features. On the other hand, cooperative learning further extracts label information by aligning input samples’ representation and output distribution with label samples. Extensive experiments on three datasets demonstrate that our method significantly improves the model’s performance compared to traditional contrastive learning methods. Moreover, our C³LPGCN outperforms state-of-the-art methods. Our source code and final models are publicly available at github
%U https://aclanthology.org/2024.findings-naacl.205
%P 3237-3247

Markdown (Informal)

[C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis](https://aclanthology.org/2024.findings-naacl.205) (He et al., Findings 2024)

• C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis (He et al., Findings 2024)

ACL

• Ye He, Shihao Zou, YuzheChen YuzheChen, and Xianying Huang. 2024. C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3237–3247, Mexico City, Mexico. Association for Computational Linguistics.