  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.
