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        <title>Exploring Graph Pre-training for Aspect-based Sentiment Analysis</title>
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        <namePart type="given">Xiaoyi</namePart>
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            <namePart type="given">Juan</namePart>
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    <abstract>Existing studies tend to extract the sentiment elements in a generative manner in order to avoid complex modeling. Despite their effectiveness, they ignore importance of the relationships between sentiment elements that could be crucial, making the large pre-trained generative models sub-optimal for modeling sentiment knowledge. Therefore, we introduce two pre-training paradigms to improve the generation model by exploring graph pre-training that targeting to strengthen the model in capturing the elements’ relationships. Specifically, We first employ an Element-level Graph Pre-training paradigm, which is designed to improve the structure awareness of the generative model. Then, we design a Task Decomposition Pre-training paradigm to make the generative model generalizable and robust against various irregular sentiment quadruples. Extensive experiments show the superiority of our proposed method, validate the correctness of our motivation.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.234</identifier>
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        <date>2023-12</date>
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            <start>3623</start>
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%0 Conference Proceedings
%T Exploring Graph Pre-training for Aspect-based Sentiment Analysis
%A Bao, Xiaoyi
%A Wang, Zhongqing
%A Zhou, Guodong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F bao-etal-2023-exploring
%X Existing studies tend to extract the sentiment elements in a generative manner in order to avoid complex modeling. Despite their effectiveness, they ignore importance of the relationships between sentiment elements that could be crucial, making the large pre-trained generative models sub-optimal for modeling sentiment knowledge. Therefore, we introduce two pre-training paradigms to improve the generation model by exploring graph pre-training that targeting to strengthen the model in capturing the elements’ relationships. Specifically, We first employ an Element-level Graph Pre-training paradigm, which is designed to improve the structure awareness of the generative model. Then, we design a Task Decomposition Pre-training paradigm to make the generative model generalizable and robust against various irregular sentiment quadruples. Extensive experiments show the superiority of our proposed method, validate the correctness of our motivation.
%R 10.18653/v1/2023.findings-emnlp.234
%U https://aclanthology.org/2023.findings-emnlp.234
%U https://doi.org/10.18653/v1/2023.findings-emnlp.234
%P 3623-3634

Markdown (Informal)

[Exploring Graph Pre-training for Aspect-based Sentiment Analysis](https://aclanthology.org/2023.findings-emnlp.234) (Bao et al., Findings 2023)

• Exploring Graph Pre-training for Aspect-based Sentiment Analysis (Bao et al., Findings 2023)

ACL

• Xiaoyi Bao, Zhongqing Wang, and Guodong Zhou. 2023. Exploring Graph Pre-training for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3623–3634, Singapore. Association for Computational Linguistics.