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LogiGAN: Learning Logical Reasoning via Adversarial Pre-training. (arXiv:2205.08794v1 [cs.CL])
May 19, 2022, 1:11 a.m. | Xinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan, Jian-Guang Lou
cs.CL updates on arXiv.org arxiv.org
We present LogiGAN, an unsupervised adversarial pre-training framework for
improving logical reasoning abilities of language models. Upon automatic
identifying logical reasoning phenomena in massive text corpus via detection
heuristics, we train language models to predict the masked-out logical
statements. Inspired by the facilitation effect of reflective thinking in human
learning, we analogically simulate the learning-thinking process with an
adversarial Generator-Verifier architecture to assist logic learning. LogiGAN
implements a novel sequential GAN approach that (a) circumvents the
non-differentiable challenge of the …
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