Abstract
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.
Abstract (translated)
我们对神经网络架构和数据传输策略在因果关系抽取中的应用进行了实证分析。通过使用不同的上下文嵌入层和架构组件进行实验，我们表明相对简单的BioBERT-BiGRU关系抽取模型比其他架构具有更好的泛化能力，适用于不同基于网页的数据源和标注策略。此外，我们引入了一个用于评估迁移性能的指标$F1_{phrase}$，该指标强调名词短语定位而非直接匹配目标标签。利用这一指标，我们可以进行数据传输实验，最终发现使用来自不同领域和标注风格的数据增强可以提高模型性能。特别地，当包含足够的隐性和显性因果句时，数据增强尤为有益。
URL
https://arxiv.org/abs/2503.06076
