| Data augmentation approaches in natural language processing: A survey B Li, Y Hou, W Che Ai Open 3, 71-90, 2022 | 224 | 2022 |
| Metaprompting: Learning to learn better prompts Y Hou, H Dong, X Wang, B Li, W Che arXiv preprint arXiv:2209.11486, 2022 | 23 | 2022 |
| Inverse is better! fast and accurate prompt for few-shot slot tagging Y Hou, C Chen, X Luo, B Li, W Che arXiv preprint arXiv:2204.00885, 2022 | 13 | 2022 |
| Semantic-Guided Image Augmentation with Pre-trained Models B Li, X Wang, X Xu, Y Hou, Y Feng, F Wang, W Che arXiv preprint, 2023 | 5 | 2023 |
| FewJoint: few-shot learning for joint dialogue understanding Y Hou, X Wang, C Chen, B Li, W Che, Z Chen International Journal of Machine Learning and Cybernetics 13 (11), 3409-3423, 2022 | 2 | 2022 |
| Mixpro: Simple yet effective data augmentation for prompt-based learning B Li, L Dou, Y Hou, Y Feng, H Mu, Q Zhu, Q Sun, W Che arXiv preprint arXiv:2304.09402, 2023 | 1 | 2023 |
| A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification Y Feng, B Li, L Qin, X Xu, W Che arXiv preprint arXiv:2304.09820, 2023 | 1 | 2023 |
