| Machine Comprehension Using Match-LSTM and Answer Pointer S Wang, J Jiang International Conference on Learning Representations (ICLR 2017), 2017 | 671 | 2017 |
| Learning Natural Language Inference with LSTM S Wang, J Jiang The 15th Annual Conference of the North American Chapter of the Association …, 2016 | 528 | 2016 |
| GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment Y Liu, D Iter, Y Xu, S Wang, R Xu, C Zhu arXiv preprint arXiv:2303.16634, 2023 | 463* | 2023 |
| R3: Reinforced Ranker-Reader for Open-Domain Question Answering S Wang, M Yu, X Guo, Z Wang, T Klinger, W Zhang, S Chang, G Tesauro, ... The 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018 | 381 | 2018 |
| An empirical study of training end-to-end vision-and-language transformers ZY Dou, Y Xu, Z Gan, J Wang, S Wang, L Wang, C Zhu, P Zhang, L Yuan, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 318 | 2022 |
| A Compare-Aggregate Model for Matching Text Sequences S Wang, J Jiang International Conference on Learning Representations (ICLR 2017), 2017 | 318 | 2017 |
| Hierarchical Graph Network for Multi-hop Question Answering Y Fang, S Sun, Z Gan, R Pillai, S Wang, J Liu arXiv preprint arXiv:1911.03631, 2019 | 208 | 2019 |
| Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering S Wang, M Yu, J Jiang, W Zhang, X Guo, S Chang, Z Wang, T Klinger, ... International Conference on Learning Representations (ICLR 2018), 2018 | 201 | 2018 |
| Generate rather than retrieve: Large language models are strong context generators W Yu, D Iter, S Wang, Y Xu, M Ju, S Sanyal, C Zhu, M Zeng, M Jiang arXiv preprint arXiv:2209.10063, 2022 | 181 | 2022 |
| Want To Reduce Labeling Cost? GPT-3 Can Help S Wang, Y Liu, Y Xu, C Zhu, M Zeng arXiv preprint arXiv:2108.13487, 2021 | 177 | 2021 |
| Prompting GPT-3 To Be Reliable C Si, Z Gan, Z Yang, S Wang, J Wang, J Boyd-Graber, L Wang arXiv preprint arXiv:2210.09150, 2022 | 176 | 2022 |
| Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models B Wang, C Xu, S Wang, Z Gan, Y Cheng, J Gao, AH Awadallah, B Li arXiv preprint arXiv:2111.02840, 2021 | 147 | 2021 |
| Clip-event: Connecting text and images with event structures M Li, R Xu, S Wang, L Zhou, X Lin, C Zhu, M Zeng, H Ji, SF Chang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 117 | 2022 |
| InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective B Wang, S Wang, Y Cheng, Z Gan, R Jia, B Li, J Liu arXiv preprint arXiv:2010.02329, 2020 | 112 | 2020 |
| A Co-Matching Model for Multi-choice Reading Comprehension S Wang, M Yu, S Chang, J Jiang Annual Meeting of the Association for Computational Linguistics (ACL), 2018 | 111 | 2018 |
| Multi-Fact Correction in Abstractive Text Summarization Y Dong, S Wang, Z Gan, Y Cheng, JCK Cheung, J Liu arXiv preprint arXiv:2010.02443, 2020 | 110 | 2020 |
| Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives Y Tay, S Wang, LA Tuan, J Fu, MC Phan, X Yuan, J Rao, SC Hui, A Zhang Annual Meeting of the Association for Computational Linguistics (ACL), 2019 | 104 | 2019 |
| Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data S Wang, Y Xu, Y Fang, Y Liu, S Sun, R Xu, C Zhu, M Zeng arXiv preprint arXiv:2203.08773, 2022 | 93 | 2022 |
| KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering D Yu, C Zhu, Y Fang, W Yu, S Wang, Y Xu, X Ren, Y Yang, M Zeng arXiv preprint arXiv:2110.04330, 2021 | 93 | 2021 |
| Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Z Wang, M Li, R Xu, L Zhou, J Lei, X Lin, S Wang, Z Yang, C Zhu, ... arXiv preprint arXiv:2205.10747, 2022 | 90 | 2022 |
Chenguang ZhuHead of Zoom GenAI Science在 zoom.us 的电子邮件经过验证
