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STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction

Wenna Lai, Haoran Xie, Guandong Xu, Qing Li

TL;DR

ASQP is challenged by data scarcity and the need to jointly predict aspect terms, categories, opinions, and sentiment. The STAR framework addresses this by stepwise task augmentation that adds quad, pairwise, and overall relation tasks, coupled with a balanced contribution loss to learn higher-order relationships from data without extra annotations. Empirical results on four benchmarks show STAR achieves superior quad prediction by effectively modeling relationships among sentiment elements, with benefits amplified by larger model sizes and careful top-$k$ order selection. The approach enhances generalization and interpretability in ABSA, offering a practical path toward robust, data-efficient ASQP systems.

Abstract

Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements construct the complete picture of sentiments. The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultaneously, hindered by difficulties in accurately coupling different sentiment elements. A key challenge is insufficient annotated data that limits the capability of models in semantic understanding and reasoning about quad prediction. To address this, we propose stepwise task augmentation and relation learning (STAR), a strategy inspired by human reasoning. STAR constructs auxiliary data to learn quadruple relationships incrementally by augmenting with pairwise and overall relation tasks derived from training data. By encouraging the model to infer causal relationships among sentiment elements without requiring additional annotations, STAR effectively enhances quad prediction. Extensive experiments demonstrate the proposed STAR exhibits superior performance on four benchmark datasets.

STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction

TL;DR

ASQP is challenged by data scarcity and the need to jointly predict aspect terms, categories, opinions, and sentiment. The STAR framework addresses this by stepwise task augmentation that adds quad, pairwise, and overall relation tasks, coupled with a balanced contribution loss to learn higher-order relationships from data without extra annotations. Empirical results on four benchmarks show STAR achieves superior quad prediction by effectively modeling relationships among sentiment elements, with benefits amplified by larger model sizes and careful top- order selection. The approach enhances generalization and interpretability in ABSA, offering a practical path toward robust, data-efficient ASQP systems.

Abstract

Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements construct the complete picture of sentiments. The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultaneously, hindered by difficulties in accurately coupling different sentiment elements. A key challenge is insufficient annotated data that limits the capability of models in semantic understanding and reasoning about quad prediction. To address this, we propose stepwise task augmentation and relation learning (STAR), a strategy inspired by human reasoning. STAR constructs auxiliary data to learn quadruple relationships incrementally by augmenting with pairwise and overall relation tasks derived from training data. By encouraging the model to infer causal relationships among sentiment elements without requiring additional annotations, STAR effectively enhances quad prediction. Extensive experiments demonstrate the proposed STAR exhibits superior performance on four benchmark datasets.

Paper Structure

This paper contains 23 sections, 3 equations, 2 figures, 4 tables.

Table of Contents

  1. Related Work
  2. Methodology
  3. Task formulation
  4. Stepwise Task Augmentation
  5. Quad Prediction.
  6. Pairwise Relation.
  7. Overall Relation.
  8. Relation Learning
  9. Training.
  10. Inference.
  11. Experiments
  12. Setup
  13. Datasets and Metrics.
  14. Implementation Details.
  15. Baselines.
  16. ...and 8 more sections

Figures (2)

  • Figure 1: An overview of the proposed STAR framework for ASQP. Compared to paraphrase and multi-view prompting methods relying on element-order prompt learning, STAR introduces stepwise task augmentation using a divide-and-conquer strategy to facilitate higher-order relation learning, which enables the progressive capture of intricate relationships among sentiment elements.
  • Figure 2: The impact of varying top-k orders on different configurations of STAR, as evaluated using the F1 score metric.