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Pre-trained models have achieved success in Chinese Short Text Matching (STM) tasks, but they often rely on superficial clues, leading to a lack of robust predictions.
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019 · 1904
Earlier work this paper cites.
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 1909
Earlier work this paper cites.
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Earlier work this paper cites.
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Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, and Kentaro Inui. 2019 · 1911
Earlier work this paper cites.
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Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 1960
Earlier work this paper cites.
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Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, and Buzhou Tang. 2018 · 1962
Earlier work this paper cites.
Temporal sequence and chinese word order
TAI James. 1985 · 1985
Earlier work this paper cites.
Pragmatic relations and word order in Chinese , volume 30
Randy J LaPolla. 1995 · 1995
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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