| MS MARCO: A human generated machine reading comprehension dataset T Nguyen, M Rosenberg, X Song, J Gao, S Tiwary, R Majumder, L Deng CoCo@ NIPS, 2016 | 505 | 2016 |
| Multi-task deep neural networks for natural language understanding X Liu, P He, W Chen, J Gao arXiv preprint arXiv:1901.11504, 2019 | 474 | 2019 |
| On the variance of the adaptive learning rate and beyond L Liu, H Jiang, P He, W Chen, X Liu, J Gao, J Han arXiv preprint arXiv:1908.03265, 2019 | 448 | 2019 |
| Unified Language Model Pre-training for Natural Language Understanding and Generation L Dong, N Yang, W Wang, F Wei, X Liu, Y Wang, J Gao, M Zhou, HW Hon arXiv preprint arXiv:1905.03197, 2019 | 354 | 2019 |
| Representation learning using multi-task deep neural networks for semantic classification and information retrieval X Liu, J Gao, X He, L Deng, K Duh, YY Wang | 329 | 2015 |
| Stochastic answer networks for machine reading comprehension X Liu, Y Shen, K Duh, J Gao arXiv preprint arXiv:1712.03556, 2017 | 152 | 2017 |
| Ms marco: A human generated machine reading comprehension dataset P Bajaj, D Campos, N Craswell, L Deng, J Gao, X Liu, R Majumder, ... arXiv preprint arXiv:1611.09268, 2016 | 149 | 2016 |
| Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization H Jiang, P He, W Chen, X Liu, J Gao, T Zhao arXiv preprint arXiv:1911.03437, 2019 | 84* | 2019 |
| Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding X Liu, P He, W Chen, J Gao arXiv preprint arXiv:1904.09482, 2019 | 84 | 2019 |
| Cyclical annealing schedule: A simple approach to mitigating kl vanishing H Fu, C Li, X Liu, J Gao, A Celikyilmaz, L Carin arXiv preprint arXiv:1903.10145, 2019 | 84 | 2019 |
| Record: Bridging the gap between human and machine commonsense reading comprehension S Zhang, X Liu, J Liu, J Gao, K Duh, B Van Durme arXiv preprint arXiv:1810.12885, 2018 | 69 | 2018 |
| Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers B Wang, R Shin, X Liu, O Polozov, M Richardson arXiv preprint arXiv:1911.04942, 2019 | 57 | 2019 |
| Unilmv2: Pseudo-masked language models for unified language model pre-training H Bao, L Dong, F Wei, W Wang, N Yang, X Liu, Y Wang, J Gao, S Piao, ... International Conference on Machine Learning, 642-652, 2020 | 49 | 2020 |
| Conversing by reading: Contentful neural conversation with on-demand machine reading L Qin, M Galley, C Brockett, X Liu, X Gao, B Dolan, Y Choi, J Gao arXiv preprint arXiv:1906.02738, 2019 | 45 | 2019 |
| Stochastic answer networks for natural language inference X Liu, K Duh, J Gao arXiv preprint arXiv:1804.07888, 2018 | 38 | 2018 |
| Language-based image editing with recurrent attentive models J Chen, Y Shen, J Gao, J Liu, X Liu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 37 | 2018 |
| Representation learning using multi-task deep neural networks J Gao, L Deng, X He, Y Wang, K Duh, X Liu US Patent 10,089,576, 2018 | 34 | 2018 |
| Towards human-level machine reading comprehension: Reasoning and inference with multiple strategies Y Xu, J Liu, J Gao, Y Shen, X Liu arXiv preprint arXiv:1711.04964, 2017 | 34* | 2017 |
| Multi-task learning with sample re-weighting for machine reading comprehension Y Xu, X Liu, Y Shen, J Liu, J Gao arXiv preprint arXiv:1809.06963, 2018 | 32 | 2018 |
| A hybrid retrieval-generation neural conversation model L Yang, J Hu, M Qiu, C Qu, J Gao, WB Croft, X Liu, Y Shen, J Liu Proceedings of the 28th ACM international conference on information and …, 2019 | 30 | 2019 |
Jianfeng GaoMicrosoft Research, RedmondVerified email at microsoft.com
