Graham Neubig
Graham Neubig
Assistant Professor of Computer Science, Carnegie Mellon University
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Dynet: The dynamic neural network toolkit
G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ...
arXiv preprint arXiv:1701.03980, 2017
259*2017
Pointwise prediction for robust, adaptable Japanese morphological analysis
G Neubig, Y Nakata, S Mori
ACL 2011, 529-533, 2011
2172011
A Syntactic Neural Model for General-Purpose Code Generation
P Yin, G Neubig
ACL 2017, 2017
1812017
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
1182015
Incorporating discrete translation lexicons into neural machine translation
P Arthur, G Neubig, S Nakamura
EMNLP 2016, 2016
962016
Neural machine translation and sequence-to-sequence models: A tutorial
G Neubig
arXiv preprint arXiv:1703.01619, 2017
912017
An unsupervised model for joint phrase alignment and extraction
G Neubig, T Watanabe, E Sumita, S Mori, T Kawahara
ACL 2011, 632-641, 2011
912011
Morphological inflection generation using character sequence to sequence learning
M Faruqui, Y Tsvetkov, G Neubig, C Dyer
NAACL 2016, 2016
862016
Safety Information Mining—What can NLP do in a disaster—
G Neubig, M Hagiwara, K Murakami, Y Matsubayashi
IJCNLP 2011, 2011
802011
Adaptation data selection using neural language models: Experiments in machine translation
K Duh, G Neubig, K Sudoh, H Tsukada
ACL 2013 2, 678-683, 2013
792013
What Do Recurrent Neural Network Grammars Learn About Syntax?
A Kuncoro, M Ballesteros, L Kong, C Dyer, G Neubig, NA Smith
EACL 2017, 2017
76*2017
Controlling output length in neural encoder-decoders
Y Kikuchi, G Neubig, R Sasano, H Takamura, M Okumura
EMNLP 2016, 2016
762016
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
712018
Controllable Invariance through Adversarial Feature Learning
Q Xie, Z Dai, Y Du, E Hovy, G Neubig
NIPS 2017, 2017
69*2017
Stronger Baselines for Trustable Results in Neural Machine Translation
M Denkowski, G Neubig
WNMT 2017, 2017
682017
Travatar: A forest-to-string machine translation engine based on tree transducers
G Neubig
ACL 2013, 91-96, 2013
672013
A POSTFILTER TO MODIFY THE MODULATION SPECTRUM IN HMM-BASED SPEECH SYNTHESIS
S Takamichi, T Toda, G Neubig, S Sakti, S Nakamura
ICASSP 2014, 2014
662014
Postfilters to modify the modulation spectrum for statistical parametric speech synthesis
S Takamichi, T Toda, AW Black, G Neubig, S Sakti, S Nakamura
TASLP 2016 24 (4), 755-767, 2016
652016
Inducing a Discriminative Parser to Optimize Machine Translation Reordering
G Neubig, T Watanabe, S Mori
EMNLP 2012, 2012
612012
The Kyoto free translation task
G Neubig
582011
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