简单模型在因果关系抽取中表现更优，数据增强提升跨领域性能
An Empirical Study of Causal Relation Extraction Transfer: Design and Data
- 采用BioBERT-BiGRU结构，结合上下文嵌入与序列建模
- 跨源迁移时，模型在不同标注策略下仍保持高泛化能力
- 引入新评估指标$F1_{phrase}$，强调名词短语定位准确性
我们对神经网络架构和数据迁移策略在因果关系抽取中的表现进行了实证分析。通过测试多种上下文嵌入层与结构组件，发现相对简单的BioBERT-BiGRU关系抽取模型在不同网页来源和标注策略下具有更强的泛化能力。此外，我们提出一个新评估指标$F1_{phrase}$，侧重于名词短语定位而非直接标签匹配。基于该指标，我们开展数据迁移实验，结果表明：混合不同领域与标注风格的数据可提升性能；尤其当数据包含适量隐含与显式因果句时，增益显著。
原文摘要 · Abstract (English)
We conduct an empirical analysis of neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that a relatively straightforward BioBERT-BiGRU relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. Furthermore, we introduce a metric for evaluating transfer performance, $F1_{phrase}$ that emphasizes noun phrase localization rather than directly matching target tags. Using this metric, we can conduct data transfer experiments, ultimately revealing that augmentation with data with varying domains and annotation styles can improve performance. Data augmentation is especially beneficial when an adequate proportion of implicitly and explicitly causal sentences are included.
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