用原型正则化联邦学习，跨领域提取情感三元组
Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction
- 客户端交换类别原型而非参数，保护隐私
- 在4个数据集上超越基线，通信成本更低
- 适合隐私敏感的跨领域情感分析场景
方面情感三元组抽取（ASTE）旨在从句子中提取方面词、观点词和情感极性组成的三元组。现有方法通常在单一数据集上独立训练，无法联合捕捉跨领域的共享特征表示。同时，数据隐私限制了集中式数据聚合。为此，我们提出基于原型的跨领域跨度原型提取（PCD-SpanProto），一种原型正则化的联邦学习框架，使分布式客户端通过交换类别级原型而非完整模型参数来协作。具体地，设计加权性能感知聚合策略与对比正则化模块，在领域异质性下提升全局原型，并促进客户端内部类紧凑性与类间可分性之间的平衡。在四个ASTE数据集上的大量实验表明，该方法优于基线并降低通信开销，验证了基于原型的跨领域知识迁移的有效性。
原文摘要 · Abstract (English)
Aspect Sentiment Triplet Extraction (ASTE) aims to extract all sentiment triplets of aspect terms, opinion terms, and sentiment polarities from a sentence. Existing methods are typically trained on individual datasets in isolation, failing to jointly capture the common feature representations shared across domains. Moreover, data privacy constraints prevent centralized data aggregation. To address these challenges, we propose Prototype-based Cross-Domain Span Prototype extraction (PCD-SpanProto), a prototype-regularized federated learning framework to enable distributed clients to exchange class-level prototypes instead of full model parameters. Specifically, we design a weighted performance-aware aggregation strategy and a contrastive regularization module to improve the global prototype under domain heterogeneity and the promotion between intra-class compactness and inter-class separability across clients. Extensive experiments on four ASTE datasets demonstrate that our method outperforms baselines and reduces communication costs, validating the effectiveness of prototype-based cross-domain knowledge transfer.
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