MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
Zhibin Gou Qingyan Guo Yujiu Yang
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.