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        <title>An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions</title>
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    <abstract>Aspect sentiment quad prediction (ASQP) is crucial in aspect-based sentiment analysis (ABSA). It involves identifying a text’s aspect,sentiment, opinion, and category. Existing methods have insufficiently explored how to effectively leverage the knowledge of pre-trainedlanguage models (PLMs) to handle implicit aspects and opinions, particularly in combinations such as implicit aspect &amp; explicit opinion, explicit aspect &amp; implicit opinion, and implicit aspect &amp; implicit opinion. We introduce ITSCL, a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad predictions, especially for implicit aspects and opinions. Implementing this approach presents several challenges. First, designing effective instructions and prompts to optimize the model’s training is difficult. Second, creating sentiment combination vectors with contrastive learning to enhance the model’s discrimination requires further investigation. To address these challenges, ITSCL combines instruction tuning with aligned PLM templates, enabling better knowledge acquisition and identification of implicit sentiments. Additionally, the contrastive learning framework enhances performance by using four fully connected layers to combine sentiments, aspects, opinions, and combinations, maximizing similarity for same-label representationsand minimizing it for different labels. Experimental results show our method significantly outperforms previous methods on benchmark datasets.</abstract>
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%0 Conference Proceedings
%T An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions
%A Zhang, Hao
%A Cheah, Yu-N
%A He, Congqing
%A Yi, Feifan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-instruction
%X Aspect sentiment quad prediction (ASQP) is crucial in aspect-based sentiment analysis (ABSA). It involves identifying a text’s aspect,sentiment, opinion, and category. Existing methods have insufficiently explored how to effectively leverage the knowledge of pre-trainedlanguage models (PLMs) to handle implicit aspects and opinions, particularly in combinations such as implicit aspect & explicit opinion, explicit aspect & implicit opinion, and implicit aspect & implicit opinion. We introduce ITSCL, a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad predictions, especially for implicit aspects and opinions. Implementing this approach presents several challenges. First, designing effective instructions and prompts to optimize the model’s training is difficult. Second, creating sentiment combination vectors with contrastive learning to enhance the model’s discrimination requires further investigation. To address these challenges, ITSCL combines instruction tuning with aligned PLM templates, enabling better knowledge acquisition and identification of implicit sentiments. Additionally, the contrastive learning framework enhances performance by using four fully connected layers to combine sentiments, aspects, opinions, and combinations, maximizing similarity for same-label representationsand minimizing it for different labels. Experimental results show our method significantly outperforms previous methods on benchmark datasets.
%U https://aclanthology.org/2024.findings-emnlp.453
%P 7698-7714

Markdown (Informal)

[An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions](https://aclanthology.org/2024.findings-emnlp.453) (Zhang et al., Findings 2024)

• An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions (Zhang et al., Findings 2024)

ACL

• Hao Zhang, Yu-N Cheah, Congqing He, and Feifan Yi. 2024. An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7698–7714, Miami, Florida, USA. Association for Computational Linguistics.