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Logical reasoning is of vital importance to natural language understanding.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Depth-first search and linear graph algorithms
Robert Endre Tarjan. 1972 · 1972
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REALM: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
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Fast random walk with restart and its applications
Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006 · 2006
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Critical thinking for language models
Gregor Betz. 2020 · 2009
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Quantifying and alleviating the language prior problem in visual question answering
Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yibing Liu, Yinglong Wang, and Mohan S. Kankanhalli. 2019 · 2019
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DREAM: A challenge dataset and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
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Look at the first sentence: Position bias in question answering
Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, Gangwoo Kim, and Jaewoo Kang. 2020 · 2020
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ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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LogiQA: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020 · 2020
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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Rept: Bridging language models and machine reading comprehension via retrieval-based pre-training
Fangkai Jiao, Yangyang Guo, Yilin Niu, Feng Ji, Feng-Lin Li, and Liqiang Nie. 2021 · 2021
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Why machine reading comprehension models learn shortcuts?
Yuxuan Lai, Chen Zhang, Yansong Feng, Quzhe Huang, and Dongyan Zhao. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Neural multi-hop reasoning with logical rules on biomedical knowledge graphs
Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Rime Raissouni, and Volker Tresp. 2021 · 2021
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Cited alongside, same era.
GCC: graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Large batch optimization for deep learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. 2020 · 2020
Cited alongside, same era.
Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2020
Cited alongside, same era.
ReasonBERT: Pre-trained to reason with distant supervision
Xiang Deng, Yu Su, Alyssa Lees, You Wu, Cong Yu, and Huan Sun. 2021 · 2021
Cited alongside, same era.
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Double graph based reasoning for document-level relation extraction
Shuang Zeng, Runxin Xu, Baobao Chang, and Lei Li. 2020a
Cited in the paper.
Siru Ouyang, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021
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ERICA: improving entity and relation understanding for pre-trained language models via contrastive learning
Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, and Jie Zhou. 2021 · 2021
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Logic-driven context extension and data augmentation for logical reasoning of text
Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, and Nan Duan. 2021 · 2021
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Discriminative reasoning for document-level relation extraction
Wang Xu, Kehai Chen, and Tiejun Zhao. 2021 · 2021
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SIRE: separate intra- and inter-sentential reasoning for document-level relation extraction
Shuang Zeng, Yuting Wu, and Baobao Chang. 2021 · 2021
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Loss re-scaling VQA: revisiting the language prior problem from a class-imbalance view
Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Qi Tian, and Min Zhang. 2022 · 2022
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