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.

从文本中提取相关原因和效果，检测因果关系的任务中，通过对三种序列标记模型进行彻底分析，并将其与基于跨度的因果性抽取方法进行比较，我们发现，与以往的复杂架构的最新模型相比，预训练语言模型（如 BERT）提供了显著的性能提升。我们观察到，基于跨度的模型在所有 4 个来自不同领域的数据集中，对于不同类型的因果短语表现更好。