Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely focused on binary classification of a text segment as causal or non-causal. In this regard, we perform a thorough analysis of three sequence tagging models for causal knowledge extraction and compare it with a span based approach to causality extraction. Our experiments show that embeddings from pre-trained language models (e.g. BERT) provide a significant performance boost on this task compared to previous state-of-the-art models with complex architectures. We observe that span based models perform better than simple sequence tagging models based on BERT across all 4 data sets from diverse domains with different types of cause-effect phrases.

翻译：因果知识抽取是通过检测因果关联，从文本中提取相关原因与结果的任务。尽管该任务对语言理解和知识发现至关重要，但近期该领域的研究主要集中于将文本片段二元分类为因果或非因果。为此，我们深入分析了三种用于因果知识抽取的序列标注模型，并将其与基于跨度的方法进行对比。实验表明，相较于先前具有复杂架构的最先进模型，预训练语言模型（如BERT）的嵌入表示在该任务上带来了显著的性能提升。我们观察到，在来自不同领域、包含多种因果短语类型的全部4个数据集上，基于跨度的方法优于基于BERT的简单序列标注模型。