| Universal adversarial triggers for attacking and analyzing NLP E Wallace, S Feng, N Kandpal, M Gardner, S Singh arXiv preprint arXiv:1908.07125, 2019 | 466 | 2019 |
| Calibrate before use: Improving few-shot performance of language models Z Zhao, E Wallace, S Feng, D Klein, S Singh International Conference on Machine Learning, 12697-12706, 2021 | 266 | 2021 |
| Pathologies of Neural Models Make Interpretations Difficult S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber EMNLP, 2018 | 238 | 2018 |
| Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber Transactions of the Association for Computational Linguistics 7, 387-401, 2019 | 113 | 2019 |
| What can ai do for me? evaluating machine learning interpretations in cooperative play S Feng, J Boyd-Graber Proceedings of the 24th International Conference on Intelligent User …, 2019 | 88 | 2019 |
| Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu COLING, 2016 | 72* | 2016 |
| Concealed data poisoning attacks on nlp models E Wallace, TZ Zhao, S Feng, S Singh arXiv preprint arXiv:2010.12563, 2020 | 63* | 2020 |
| Knowledge-based semantic embedding for machine translation C Shi, S Liu, S Ren, S Feng, M Li, M Zhou, X Sun, H Wang Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 63 | 2016 |
| Interpreting neural networks with nearest neighbors E Wallace, S Feng, J Boyd-Graber arXiv preprint arXiv:1809.02847, 2018 | 40 | 2018 |
| Understanding impacts of high-order loss approximations and features in deep learning interpretation S Singla, E Wallace, S Feng, S Feizi International Conference on Machine Learning, 5848-5856, 2019 | 39 | 2019 |
| Misleading failures of partial-input baselines S Feng, E Wallace, J Boyd-Graber arXiv preprint arXiv:1905.05778, 2019 | 33 | 2019 |
| Quizbowl: The case for incremental question answering P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber arXiv preprint arXiv:1904.04792, 2019 | 30 | 2019 |
| A.; Iyyer, M.; Rodriguez, P.; and Boyd-Graber, J. 2018. Pathologies of neural models make interpretations difficult S Feng, E Wallace, II Grissom proceedings of the 2018 conference on empirical methods in natural language …, 0 | 15 | |
| Human-computer question answering: The case for quizbowl J Boyd-Graber, S Feng, P Rodriguez The NIPS'17 Competition: Building Intelligent Systems, 169-180, 2018 | 10 | 2018 |
| How pre-trained word representations capture commonsense physical comparisons P Goel, S Feng, J Boyd-Graber Proceedings of the First Workshop on Commonsense Inference in Natural …, 2019 | 6 | 2019 |
| Machine explanations and human understanding C Chen, S Feng, A Sharma, C Tan arXiv preprint arXiv:2202.04092, 2022 | 5 | 2022 |
| Active example selection for in-context learning Y Zhang, S Feng, C Tan arXiv preprint arXiv:2211.04486, 2022 | 4 | 2022 |
| The umd neural machine translation systems at wmt17 bandit learning task A Sharaf, S Feng, K Nguyen, K Brantley, H Daumé III arXiv preprint arXiv:1708.01318, 2017 | 4 | 2017 |
| Introduction to NIPS 2017 Competition Track S Escalera, M Weimer, M Burtsev, V Malykh, V Logacheva, R Lowe, ... The NIPS'17 Competition: Building Intelligent Systems, 1-23, 2018 | 1 | 2018 |
| Learning Human-Compatible Representations for Case-Based Decision Support H Liu, Y Tian, C Chen, S Feng, Y Chen, C Tan arXiv preprint arXiv:2303.04809, 2023 | | 2023 |
Eric WallaceUC BerkeleyVerified email at berkeley.edu
