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This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge.
BERT has a moral compass: Improvements of ethical and moral values of machines
Patrick Schramowski, Cigdem Turan, Sophie F. Jentzsch, Constantin A. Rothkopf, and Kristian Kersting. 2019 · 1912
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Ethical by design: Ethics best practices for natural language processing
Jochen L. Leidner and Vassilis Plachouras. 2017 · 2017
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Language (technology) is power: A critical survey of ”bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna M. Wallach. 2020 · 2020
Earlier work this paper cites.
Revisiting pre-trained models for chinese natural language processing
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Dice loss for data-imbalanced NLP tasks
Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020 · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu. 2020 · 2020
Cited alongside, same era.
CLUE: A Chinese language understanding evaluation benchmark
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, and Zhenzhong Lan. 2020 · 2020
Later among the works it cites.
Pre-training with whole word masking for chinese bert
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, and Ziqing Yang. 2021 · 2021
Later among the works it cites.
Deep learning for semantic matching: A survey
Han Li, Yash Govind, Sidharth Mudgal, Theodoros Rekatsinas, and AnHai Doan. 2021 · 2021
Later among the works it cites.
Large pre-trained language models contain human-like biases of what is right and wrong to do
Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting. 2022 · 2022
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