Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. We present a human-annotated explainable CAusal REasoning dataset (e-CARE)
e-CARE contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions.
We've built a few resources to help you get started with the dataset.
Download a copy of the dataset in jsonl format:
Read the following Readme to get familiar with the data structure.
The test set of e-CARE is a blind set, you should following the instruction to get the performace on test set. And the submitted models will be added to the leaderboard with the premission of the author.
We provide two evaluation scripts for obtaining evaluation metrics on dev and test sets. The format of prediction files can refer to github.
Ask us questions at our ldu@ir.hit.edu.cn or at kxiong@ir.hit.edu.cn.
We thank the SQuAD team and theReCoRD team for using their code and templates for generating this website..
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