We introduce a novel fine-grained causal reasoning dataset and present a series of novel tasks in NLP, from causality detection to event causality extraction and Causal QA.
Our dataset contains human annotations of 25K cause-effect event pairs and 24K question-answering pairs within multi-sentence samples.
We consider three useful fine-grained causalities:
- Cause
- Enable
- Prevent