Measuring and relieving the over-smoothing problem for graph neural networks from the topological view D Chen, Y Lin, W Li, P Li, J Zhou, X Sun Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3438-3445, 2020 | 1009 | 2020 |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset Y Yao, D Ye, P Li, X Han, Y Lin, Z Liu, Z Liu, L Huang, J Zhou, M Sun Proceedings of the 57th Conference of the Association for Computational …, 2019 | 507 | 2019 |
Clustering to find exemplar terms for keyphrase extraction Z Liu, P Li, Y Zheng, M Sun Proceedings of the 2009 conference on empirical methods in natural language …, 2009 | 432 | 2009 |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification T Gao, X Han, H Zhu, Z Liu, P Li, M Sun, J Zhou arXiv preprint arXiv:1910.07124, 2019 | 259 | 2019 |
Deep recurrent models with fast-forward connections for neural machine translation J Zhou, Y Cao, X Wang, P Li, W Xu Transactions of the Association for Computational Linguistics 4, 371-383, 2016 | 259 | 2016 |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction H Peng, T Gao, X Han, Y Lin, P Li, Z Liu, M Sun, J Zhou arXiv preprint arXiv:2010.01923, 2020 | 182 | 2020 |
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer F Luo, P Li, J Zhou, P Yang, B Chang, X Sun, Z Sui Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 180 | 2019 |
Coreferential reasoning learning for language representation D Ye, Y Lin, J Du, Z Liu, P Li, M Sun, Z Liu arXiv preprint arXiv:2004.06870, 2020 | 178 | 2020 |
MAVEN: A Massive General Domain Event Detection Dataset X Wang, Z Wang, X Han, W Jiang, R Han, Z Liu, J Li, P Li, Y Lin, J Zhou arXiv preprint arXiv:2004.13590, 2020 | 168 | 2020 |
Adversarial Training for Weakly Supervised Event Detection X Wang, X Han, Z Liu, M Sun, P Li Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 164 | 2019 |
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention X Han, P Yu, Z Liu, M Sun, P Li Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 164 | 2018 |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction X Han, T Gao, Y Lin, H Peng, Y Yang, C Xiao, Z Liu, P Li, M Sun, J Zhou arXiv preprint arXiv:2004.03186, 2020 | 148 | 2020 |
Packed Levitated Marker for Entity and Relation Extraction D Ye, Y Lin, P Li, M Sun Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 120 | 2022 |
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning Y Qin, Y Lin, R Takanobu, Z Liu, P Li, H Ji, M Huang, M Sun, J Zhou arXiv preprint arXiv:2012.15022, 2020 | 119 | 2020 |
NumNet: Machine Reading Comprehension with Numerical Reasoning Q Ran, Y Lin, P Li, J Zhou, Z Liu arXiv preprint arXiv:1910.06701, 2019 | 117 | 2019 |
On transferability of prompt tuning for natural language processing Y Su, X Wang, Y Qin, CM Chan, Y Lin, H Wang, K Wen, Z Liu, P Li, J Li, ... Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 114 | 2022 |
HMEAE: Hierarchical modular event argument extraction X Wang, Z Wang, X Han, Z Liu, J Li, P Li, M Sun, J Zhou, X Ren Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 101 | 2019 |
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering P Li, W Li, Z He, X Wang, Y Cao, J Zhou, W Xu arXiv preprint arXiv:1607.06275, 2016 | 99 | 2016 |
CLEVE: Contrastive Pre-training for Event Extraction Z Wang, X Wang, X Han, Y Lin, L Hou, Z Liu, P Li, J Li, J Zhou arXiv preprint arXiv:2105.14485, 2021 | 98 | 2021 |
Recursive autoencoders for ITG-based translation P Li, Y Liu, M Sun Proceedings of the 2013 Conference on Empirical Methods in Natural Language …, 2013 | 94 | 2013 |