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Towards Fine-grained Causal Reasoning and QA. (arXiv:2204.07408v1 [cs.CL])
April 18, 2022, 1:11 a.m. | Linyi Yang, Zhen Wang, Yuxiang Wu, Jie Yang, Yue Zhang
cs.CL updates on arXiv.org arxiv.org
Understanding causality is key to the success of NLP applications, especially
in high-stakes domains. Causality comes in various perspectives such as enable
and prevent that, despite their importance, have been largely ignored in the
literature. This paper introduces a novel fine-grained causal reasoning dataset
and presents a series of novel predictive tasks in NLP, such as causality
detection, 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 …
More from arxiv.org / cs.CL updates on arXiv.org
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