Shi Feng
Shi Feng
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Citeras av
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
arXiv preprint arXiv:1908.07125, 2019
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP, 2018
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
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
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
Calibrate before use: Improving few-shot performance of language models
TZ Zhao, E Wallace, S Feng, D Klein, S Singh
arXiv preprint arXiv:2102.09690, 2021
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
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
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
arXiv preprint arXiv:1905.05778, 2019
Interpreting neural networks with nearest neighbors
E Wallace, S Feng, J Boyd-Graber
arXiv preprint arXiv:1809.02847, 2018
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
Concealed Data Poisoning Attacks on NLP Models
E Wallace, TZ Zhao, S Feng, S Singh
arXiv preprint arXiv:2010.12563, 2020
Customizing Triggers with Concealed Data Poisoning
E Wallace, TZ Zhao, S Feng, S Singh
arXiv e-prints, arXiv: 2010.12563, 2020
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
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
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
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
Human Learning Meets Representation Learning
M Shu, S Feng, J Boyd-Graber
Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering Open Website
E Wallace, P Rodriguez, S Feng, I Yamada, JL Boyd-Graber
Quizbowl: The Case for Incremental Question Answering Open Website
P Rodriguez, S Feng, M Iyyer, H He, JL Boyd-Graber
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