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Graham Neubig
Graham Neubig
Carnegie Mellon University, Inspired Cognition
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P Liu, W Yuan, J Fu, Z Jiang, H Hayashi, G Neubig
ACM Computing Surveys, 2021
12022021
Are Sixteen Heads Really Better than One?
P Michel, O Levy, G Neubig
NeurIPS 2019, 2019
6502019
XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalization
J Hu, S Ruder, A Siddhant, G Neubig, O Firat, M Johnson
ICML 2020, 2020
6222020
How can we know what language models know?
Z Jiang, FF Xu, J Araki, G Neubig
TACL 8, 423-438, 2020
6132020
A Syntactic Neural Model for General-Purpose Code Generation
P Yin, G Neubig
ACL 2017, 2017
6112017
Dynet: The dynamic neural network toolkit
G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ...
arXiv preprint arXiv:1701.03980, 2017
424*2017
Pointwise prediction for robust, adaptable Japanese morphological analysis
G Neubig, Y Nakata, S Mori
ACL 2011, 529-533, 2011
3292011
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
Y Qi, DS Sachan, M Felix, SJ Padmanabhan, G Neubig
NAACL 2018, 2018
3202018
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
P Yin, G Neubig, W Yih, S Riedel
ACL 2020, 2020
3022020
Learning to generate pseudo-code from source code using statistical machine translation (t)
Y Oda, H Fudaba, G Neubig, H Hata, S Sakti, T Toda, S Nakamura
ASE 2015, 574-584, 2015
3002015
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
J He, D Spokoyny, G Neubig, T Berg-Kirkpatrick
ICLR 2019, 2019
2772019
Stress Test Evaluation for Natural Language Inference
A Naik, A Ravichander, N Sadeh, C Rose, G Neubig
COLING 2018, 2018
2772018
Controllable Invariance through Adversarial Feature Learning
Q Xie, Z Dai, Y Du, E Hovy, G Neubig
NIPS 2017, 2017
2542017
Towards a unified view of parameter-efficient transfer learning
J He, C Zhou, X Ma, T Berg-Kirkpatrick, G Neubig
ICLR 2022, 2022
2302022
Competence-based Curriculum Learning for Neural Machine Translation
EA Platanios, O Stretcu, G Neubig, B Poczos, TM Mitchell
NAACL 2019, 2019
2282019
Controlling output length in neural encoder-decoders
Y Kikuchi, G Neubig, R Sasano, H Takamura, M Okumura
EMNLP 2016, 2016
2272016
BARTScore: Evaluating Generated Text as Text Generation
W Yuan, G Neubig, P Liu
NeurIPS 2021, 2021
2212021
Neural machine translation and sequence-to-sequence models: A tutorial
G Neubig
arXiv preprint arXiv:1703.01619, 2017
2142017
Weight Poisoning Attacks on Pre-trained Models
K Kurita, P Michel, G Neubig
ACL 2020, 2020
2112020
Incorporating discrete translation lexicons into neural machine translation
P Arthur, G Neubig, S Nakamura
EMNLP 2016, 2016
2082016
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