| Dialogpt: Large-Scale generative pre-training for conversational response generation Y Zhang arXiv preprint arXiv:1911.00536, 2019 | 1645 | 2019 |
| Towards unifying Hamiltonian Monte Carlo and slice sampling Y Zhang, X Wang, C Chen, R Henao, K Fan, L Carin Advances in Neural Information Processing Systems, 1741-1749, 2016 | 1341* | 2016 |
| What Makes Good In-Context Examples for GPT-? J Liu, D Shen, Y Zhang, B Dolan, L Carin, W Chen arXiv preprint arXiv:2101.06804, 2021 | 1286 | 2021 |
| Adversarial feature matching for text generation Y Zhang, Z Gan, K Fan, Z Chen, R Henao, D Shen, L Carin arXiv preprint arXiv:1706.03850, 2017 | 682* | 2017 |
| Joint embedding of words and labels for text classification G Wang, C Li, W Wang, Y Zhang, D Shen, X Zhang, R Henao, L Carin arXiv preprint arXiv:1805.04174, 2018 | 552 | 2018 |
| Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms D Shen, G Wang, W Wang, MR Min, Q Su, Y Zhang, C Li, R Henao, ... arXiv preprint arXiv:1805.09843, 2018 | 454 | 2018 |
| Generating informative and diverse conversational responses via adversarial information maximization Y Zhang, M Galley, J Gao, Z Gan, X Li, C Brockett, B Dolan Advances in Neural Information Processing Systems 31, 2018 | 319 | 2018 |
| Contextualized perturbation for textual adversarial attack D Li, Y Zhang, H Peng, L Chen, C Brockett, MT Sun, B Dolan arXiv preprint arXiv:2009.07502, 2020 | 256 | 2020 |
| Optimus: Organizing sentences via pre-trained modeling of a latent space C Li, X Gao, Y Li, B Peng, X Li, Y Zhang, J Gao arXiv preprint arXiv:2004.04092, 2020 | 188 | 2020 |
| Zero-shot learning via class-conditioned deep generative models W Wang, Y Pu, V Verma, K Fan, Y Zhang, C Chen, P Rai, L Carin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 181 | 2018 |
| Adversarial text generation via feature-mover's distance L Chen, S Dai, C Tao, H Zhang, Z Gan, D Shen, Y Zhang, G Wang, ... Advances in neural information processing systems 31, 2018 | 171 | 2018 |
| Triangle generative adversarial networks Z Gan, L Chen, W Wang, Y Pu, Y Zhang, H Liu, C Li, L Carin Advances in neural information processing systems 30, 2017 | 163 | 2017 |
| A token-level reference-free hallucination detection benchmark for free-form text generation T Liu, Y Zhang, C Brockett, Y Mao, Z Sui, W Chen, B Dolan arXiv preprint arXiv:2104.08704, 2021 | 125 | 2021 |
| Deconvolutional paragraph representation learning Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin Advances in Neural Information Processing Systems 30, 2017 | 120 | 2017 |
| POINTER: Constrained progressive text generation via insertion-based generative pre-training Y Zhang, G Wang, C Li, Z Gan, C Brockett, B Dolan arXiv preprint arXiv:2005.00558, 2020 | 117 | 2020 |
| Improving disentangled text representation learning with information-theoretic guidance P Cheng, MR Min, D Shen, C Malon, Y Zhang, Y Li, L Carin arXiv preprint arXiv:2006.00693, 2020 | 109 | 2020 |
| Improving sequence-to-sequence learning via optimal transport L Chen, Y Zhang, R Zhang, C Tao, Z Gan, H Zhang, B Li, D Shen, C Chen, ... arXiv preprint arXiv:1901.06283, 2019 | 108 | 2019 |
| Dialogue response ranking training with large-scale human feedback data X Gao, Y Zhang, M Galley, C Brockett, B Dolan arXiv preprint arXiv:2009.06978, 2020 | 102 | 2020 |
| Jointly optimizing diversity and relevance in neural response generation X Gao, S Lee, Y Zhang, C Brockett, M Galley, J Gao, B Dolan arXiv preprint arXiv:1902.11205, 2019 | 97 | 2019 |
| A controllable model of grounded response generation Z Wu, M Galley, C Brockett, Y Zhang, X Gao, C Quirk, R Koncel-Kedziorski, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (16), 14085 …, 2021 | 90 | 2021 |
Bill DolanPartner Research Manager, Microsoft ResearchVerified email at microsoft.com
