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### Reasoning
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### Reasoning
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> **[LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](logic_gan)**
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> **[LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](logic_gan)**
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Xinyu Pi*, Wanjun Zhong*, Yan Gao, Nan Duan, Jian-Guang Lou, NeurIPS 2022.
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Xinyu Pi*, Wanjun Zhong*, Yan Gao, Nan Duan, Jian-Guang Lou, NeurIPS 2022.
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>>>>>>> origin/logigan
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### Text-to-SQL
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### Text-to-SQL
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> **[UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](unified_parser_text_to_sql)**
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> **[UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](unified_parser_text_to_sql)**
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# LogiGAN
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This repository serves primarily as codebase and data, model for training, evaluation and inference of the logical pre-training method LogiGAN.
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[LogiGAN](https://arxiv.org/abs/2205.08794) (NeurIPS 2022) is the adversarial logical pre-training method with Transformer-based encoder-decoder backbone.
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# Preprocessing
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# Citation
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If you find this resource useful, please cite the paper introducing CARP:
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```
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@article{pi2022logigan,
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title={LogiGAN: Learning Logical Reasoning via Adversarial Pre-training},
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author={Pi, Xinyu and Zhong, Wanjun and Gao, Yan and Duan, Nan and Lou, Jian-Guang},
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journal={arXiv preprint arXiv:2205.08794},
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year={2022}
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}
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```
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