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Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms.
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Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, and Daniel Gildea. 2018 · 2018
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Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi. 2019 · 2019
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Hongyu Ren and Jure Leskovec. 2020 · 2020
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Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2020
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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, M. Zhou, Jiahai Wang, and Jian Yin. 2020 · 2020
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DAGN: Discourse-aware graph network for logical reasoning
Yinya Huang, Meng Fang, Yu Cao, Liwei Wang, and Xiaodan Liang. 2021 · 2021
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Why machine reading comprehension models learn shortcuts?
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Logic-driven context extension and data augmentation for logical reasoning of text
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Modeling hierarchical reasoning chains by linking discourse units and key phrases for reading comprehension
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Neural-symbolic learning and reasoning: A survey and interpretation
Artur d’Avila Garcez, Sebastian Bader, Howard Bowman, Luis C Lamb, Leo de Penning, BV Illuminoo, Hoifung Poon, and COPPE Gerson Zaverucha. 2022 · 2022
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Merit: Meta-path guided contrastive learning for logical reasoning
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