Diagnosing the first-order logical reasoning ability through logicnli J Tian, Y Li, W Chen, L Xiao, H He, Y Jin Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 45 | 2021 |
De-confounded variational encoder-decoder for logical table-to-text generation W Chen, J Tian, Y Li, H He, Y Jin Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 27 | 2021 |
Weakly supervised neural symbolic learning for cognitive tasks J Tian, Y Li, W Chen, L Xiao, H He, Y Jin Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5888-5896, 2022 | 11 | 2022 |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. Y Li, J Tian, W Chen, C Fan, H He, Y Jin Proceedings of the 29th International Conference on Computational …, 2022 | 4 | 2022 |
A generative-symbolic model for logical reasoning in nlu J Tian, Y Li, W Chen, HE Hao, Y Jin Is Neuro-Symbolic SOTA still a myth for Natural Language Inference? The …, 2021 | 2 | 2021 |
MTR: A dataset fusing inductive, deductive, and defeasible reasoning Y Li, J Tian, C Fan, W Chen, H He, Y Jin Findings of the Association for Computational Linguistics: ACL 2023, 10078-10089, 2023 | 1 | 2023 |
Exploring Multi-hop Reasoning Process in NLU from the View of Bayesian Probability Y Li, J Tian, HE Hao, Y Jin Is Neuro-Symbolic SOTA still a myth for Natural Language Inference? The …, 0 | | |