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.

本文介绍了 Multi-view Prompting （MvP） 的概念，该方法通过不同的视角聚合在指定格式中生成的情感元素，可以有效地模拟多视角和多任务。MvP 通过元素顺序提示，引导语言模型生成多个情感元组，并通过投票选择最合理的元组。研究表明，该方法在 4 项基准任务中的 10 个数据集上，甚至在低资源设置中都显著提升了现有最先进的性能，并得到了广泛的评价验证，证实了 MvP 的有效性、灵活性和跨任务可转移性。