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About this paper
Zhibin Gou, Qingyan Guo, Yujiu Yang
Tsinghua University researchers developed Multi-view Prompting (MVP), an approach for Aspect-Based Sentiment Analysis that generates sentiment tuples from multiple element order perspectives and aggregates the results. This method achieved state-of-the-art performance across 10 datasets in 4 ABSA tasks, showing robust improvements especially in low-resource settings.
Key insights
- Generating sentiment elements from multiple permutations (views) and aggregating them effectively mitigates the limitations of fixed-order generation, improving robustness.
- The aggregation process reduces error accumulation and reliance on any single generation path, leading to more accurate predictions.
- A single model can handle various Aspect-Based Sentiment Analysis tasks through multi-tasking, leveraging shared knowledge across tasks, particularly benefiting low-resource scenarios.
Summary by alphaXiv.
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