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Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
Joint reasoning for temporal and causal relations
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
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Annotating causality in the tempeval-3 corpus
Paramita Mirza, Rachele Sprugnoli, Sara Tonelli, and Manuela Speranza. 2014 · 2014
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Commonsense causal reasoning between short texts
Zhiyi Luo, Yuchen Sha, Kenny Q Zhu, Seung-won Hwang, and Zhongyuan Wang. 2016 · 2016
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Adversarial training methods for semi-supervised text classification
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Caters: Causal and temporal relation scheme for semantic annotation of event structures
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The event storyline corpus: A new benchmark for causal and temporal relation extraction
Tommaso Caselli and Piek Vossen. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Zhongyang Li, Xiao Ding, Ting Liu, J Edward Hu, and Benjamin Van Durme. 2020 · 2020
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Winogrande: An adversarial winograd schema challenge at scale
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Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
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