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

2025-01-27

AI Summary

To address the scarcity of annotated data in Aspect-Sentiment Quadruple Prediction (ASQP), which hinders modeling the coupled relationships among aspect terms, aspect categories, opinion terms, and sentiment polarities, this paper proposes a progressive task-augmentation framework that requires no additional labeling. Our method decomposes quadruple prediction into two auxiliary tasks—pairwise relation identification and holistic structural reasoning—to emulate human-like incremental semantic understanding. It leverages multi-stage prompt tuning, relation-aware loss optimization, and self-supervised knowledge distillation to inject relational priors zero-shot and achieve semantic decoupling. Evaluated on four benchmark datasets, our approach improves quadruple F1 scores by 3.2–5.8 percentage points over strong baselines, demonstrating the effectiveness of relation-guided augmentation for low-resource sentiment structure modeling.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Aspect-based Sentiment Quadruple Prediction
• Data Sparsity
• Model Accuracy

Innovation

• STAR Strategy
• Incremental Task Difficulty
• Relational Learning