首个联合提取观点与情感的过渡模型，提升准确率并降低计算开销。
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
- 基于转换机制联合抽取观点与情感，避免误差传播。
- 多数据集训练下F1值超越现有模型，提升显著。
- 适合需要高效跨领域情感分析的研究者使用。
方面-观点对抽取（AOPE）和方面情感三元组抽取（ASTE）在自然语言处理中备受关注。然而，现有方法通常独立抽取方面与观点，仅可选添加成对关系，常导致误差传播且时间复杂度高。受基于转换的依存句法解析启发，我们提出首个用于AOPE和ASTE的转换模型，实现方面与观点的联合抽取，更好捕捉位置感知的方面-观点关系，并缓解实体级偏差。通过集成对比增强优化，模型实现更精准的动作预测，并在线性时间内联合优化各子任务。在4个常用ASTE/AOPE数据集上的大量实验表明：虽然在单一数据集上训练时表现略逊于部分先前模型，但在组合数据集上训练时，本模型在ASTE和AOPE任务上均达到最佳性能，F1值显著优于最强现有模型。我们推测这是由于模型能从多个数据集和领域中学习转换动作。代码已公开于https://anonymous.4open.science/r/trans_aste-8FCF。
原文摘要 · Abstract (English)
Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these challenges and being inspired by transition-based dependency parsing, we propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly, which also better captures position-aware aspect-opinion relations and mitigates entity-level bias. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions and jointly optimizes separate subtasks in linear time. Extensive experiments on 4 commonly used ASTE/AOPE datasets show that, while performing worse when trained on a single dataset than some previous models, our model achieves the best performance on both ASTE and AOPE if trained on combined datasets, outperforming the strongest previous models in F1-measures (often by a large margin). We hypothesize that this is due to our model's ability to learn transition actions from multiple datasets and domains. Our code is available at https://anonymous.4open.science/r/trans_aste-8FCF.
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