| Tinybert: Distilling bert for natural language understanding X Jiao, Y Yin, L Shang, X Jiang, X Chen, L Li, F Wang, Q Liu Findings of the association for computational linguistics: EMNLP 2020, 4163-4174, 2020 | 2650 | 2020 |
| ERNIE: Enhanced Language Representation with Informative Entities Z Zhang, X Han, Z Liu, X Jiang, M Sun, Q Liu arXiv preprint arXiv:1905.07129, 2019 | 2139 | 2019 |
| FILIP: Fine-grained Interactive Language-Image Pre-Training L Yao, R Huang, L Hou, G Lu, M Niu, H Xu, X Liang, Z Li, X Jiang, C Xu arXiv preprint arXiv:2111.07783, 2021 | 832 | 2021 |
| Aligning Large Language Models with Human: A Survey Y Wang, W Zhong, L Li, F Mi, X Zeng, W Huang, L Shang, X Jiang, Q Liu arXiv preprint arXiv:2307.12966, 2023 | 515 | 2023 |
| Dynabert: Dynamic bert with adaptive width and depth L Hou, Z Huang, L Shang, X Jiang, X Chen, Q Liu Advances in Neural Information Processing Systems 33, 2020 | 398 | 2020 |
| PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation W Zeng, X Ren, T Su, H Wang, Y Liao, Z Wang, X Jiang, ZZ Yang, K Wang, ... arXiv preprint arXiv:2104.12369, 2021 | 309 | 2021 |
| Binarybert: Pushing the limit of bert quantization H Bai, W Zhang, L Hou, L Shang, J Jin, X Jiang, Q Liu, M Lyu, I King Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 307 | 2021 |
| Paraphrase generation with deep reinforcement learning Z Li, X Jiang, L Shang, H Li Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 278 | 2018 |
| Neural generative question answering J Yin, X Jiang, Z Lu, L Shang, H Li, X Li Proceedings of the Workshop on Human-Computer Question Answering, 36-42, 2016 | 275 | 2016 |
| TernaryBERT: Distillation-aware Ultra-low Bit BERT W Zhang, L Hou, Y Yin, L Shang, X Chen, X Jiang, Q Liu arXiv preprint arXiv:2009.12812, 2020 | 255 | 2020 |
| A ranking approach to keyphrase extraction X Jiang, Y Hu, H Li Proceedings of the 32nd international ACM SIGIR conference on Research and …, 2009 | 216 | 2009 |
| Wukong: A 100 million large-scale chinese cross-modal pre-training benchmark J Gu, X Meng, G Lu, L Hou, N Minzhe, X Liang, L Yao, R Huang, W Zhang, ... Advances in Neural Information Processing Systems 35, 26418-26431, 2022 | 210 | 2022 |
| Affective neural response generation N Asghar, P Poupart, J Hoey, X Jiang, L Mou European Conference on Information Retrieval, 154-166, 2018 | 207 | 2018 |
| BERT-MK: Integrating graph contextualized knowledge into pre-trained language models B He, D Zhou, J Xiao, X Jiang, Q Liu, NJ Yuan, T Xu Findings of the Association for Computational Linguistics: EMNLP 2020, 2281-2290, 2020 | 196 | 2020 |
| On position embeddings in bert B Wang, L Shang, C Lioma, X Jiang, H Yang, Q Liu, JG Simonsen International Conference on Learning Representations, 2020 | 180 | 2020 |
| SYNCOBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation X Wang, FM Yasheng Wang, P Zhou, Y Wan, X Liu, L Li, H Wu, J Liu, ... | 167 | 2021 |
| Generate & Rank: A Multi-task Framework for Math Word Problems J Shen, Y Yin, L Li, L Shang, X Jiang, M Zhang, Q Liu arXiv preprint arXiv:2109.03034, 2021 | 161 | 2021 |
| NEZHA: Neural Contextualized Representation for Chinese Language Understanding J Wei, X Ren, X Li, W Huang, Y Liao, Y Wang, J Lin, X Jiang, X Chen, ... arXiv preprint arXiv:1909.00204, 2019 | 156 | 2019 |
| Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation W Dai, L Hou, L Shang, X Jiang, Q Liu, P Fung arXiv preprint arXiv:2203.06386, 2022 | 135 | 2022 |
| PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing X Ren, P Zhou, X Meng, X Huang, Y Wang, W Wang, P Li, X Zhang, ... arXiv preprint arXiv:2303.10845, 2023 | 128 | 2023 |
Qun LiuHuaweiVerified email at huawei.com
