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        <title>MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction</title>
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    <abstract>Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MVP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MVP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MVP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MVP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MVP.</abstract>
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%0 Conference Proceedings
%T MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
%A Gou, Zhibin
%A Guo, Qingyan
%A Yang, Yujiu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gou-etal-2023-mvp
%X Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MVP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MVP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MVP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MVP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MVP.
%R 10.18653/v1/2023.acl-long.240
%U https://aclanthology.org/2023.acl-long.240
%U https://doi.org/10.18653/v1/2023.acl-long.240
%P 4380-4397

Markdown (Informal)

[MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://aclanthology.org/2023.acl-long.240) (Gou et al., ACL 2023)

• MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (Gou et al., ACL 2023)

ACL

• Zhibin Gou, Qingyan Guo, and Yujiu Yang. 2023. MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4380–4397, Toronto, Canada. Association for Computational Linguistics.