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Generative methods greatly promote aspect-based sentiment analysis viagenerating a sequence of sentiment elements in a specified format. However,existing studies usually predict sentiment elements in a fixed order, whichignores the effect of the interdependence of the elements in a sentiment tupleand the diversity of language expression on the results. In this work, wepropose Multi-view Prompting (MvP) that aggregates sentiment elements generatedin different orders, leveraging the intuition of human-like problem-solvingprocesses from different views. Specifically, MvP introduces element orderprompts to guide the language model to generate multiple sentiment tuples, eachwith a different element order, and then selects the most reasonable tuples byvoting. MvP can naturally model multi-view and multi-task as permutations andcombinations of elements, respectively, outperforming previous task-specificdesigned methods on multiple ABSA tasks with a single model. Extensiveexperiments show that MvP significantly advances the state-of-the-artperformance on 10 datasets of 4 benchmark tasks, and performs quite effectivelyin low-resource settings. Detailed evaluation verified the effectiveness,flexibility, and cross-task transferability of MvP.

MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction