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Shi Feng
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Year
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
arXiv preprint arXiv:1908.07125, 2019
3332019
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP, 2018
1982018
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
1122021
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
81*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
722019
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
69*2016
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
532016
Concealed data poisoning attacks on nlp models
E Wallace, TZ Zhao, S Feng, S Singh
arXiv preprint arXiv:2010.12563, 2020
33*2020
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
322019
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
arXiv preprint arXiv:1905.05778, 2019
302019
Interpreting neural networks with nearest neighbors
E Wallace, S Feng, J Boyd-Graber
arXiv preprint arXiv:1809.02847, 2018
302018
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
192019
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
10
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
82018
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
52019
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
42017
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
12018
Human Learning Meets Representation Learning
M Shu, S Feng, J Boyd-Graber
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Articles 1–18