Graham Neubig

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Satoshi NakamuraNara Institute of Science and TechnologyVerified email at is.naist.jp
Sakriani SaktiProfessor, Nara Institute of Science and TechnologyVerified email at is.naist.jp
Tomoki TodaNagoya UniversityVerified email at icts.nagoya-u.ac.jp
Pengfei LiuAssociate professor at Shanghai Jiao Tong UniversityVerified email at andrew.cmu.edu
Zhengbao JiangCarnegie Mellon UniversityVerified email at cs.cmu.edu
Antonis AnastasopoulosAssistant Professor, George Mason UniversityVerified email at gmu.edu
Pengcheng YinGoogle DeepmindVerified email at google.com
Fangzheng (Frank) XuCarnegie Mellon UniversityVerified email at cs.cmu.edu
Paul MichelResearch Scientist, DeepMindVerified email at deepmind.com
Junxian HeHong Kong University of Science and TechnologyVerified email at cse.ust.hk
Chunting ZhouFacebook AI ResearchVerified email at meta.com
Junjie HuAssistant Professor, University of Wisconsin-MadisonVerified email at wisc.edu
Xuezhe MaInformation Sciences Institute, University of Southern CaliforniaVerified email at usc.edu
Shuyan ZhouCarnegie Mellon UniversityVerified email at cs.cmu.edu
Taylor Berg-KirkpatrickUniversity of California San DiegoVerified email at ucsd.edu
Hiroaki HayashiSalesforce ResearchVerified email at salesforce.com
Weizhe YuanNew York UniversityVerified email at nyu.edu
Yusuke OdaNAISTVerified email at naist.ac.jp
Xinyi WangResearch Scientist, Google DeepMindVerified email at google.com
Uri AlonResearch Scientist, Google DeepMindVerified email at google.com

Graham Neubig

Carnegie Mellon University, All Hands AI

Verified email at cs.cmu.edu - Homepage

Natural Language Processing Machine Learning Artificial Intelligence


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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	4155	2021
How can we know what language models know? Z Jiang, FF Xu, J Araki, G Neubig TACL 8, 423-438, 2020	1286	2020
NusaCrowd: Open source initiative for Indonesian NLP resources S Cahyawijaya, H Lovenia, AF Aji, G Winata, B Wilie, F Koto, R Mahendra, ... Findings of the Association for Computational Linguistics: ACL 2023, 13745-13818, 2023	1091	2023
Are Sixteen Heads Really Better than One? P Michel, O Levy, G Neubig NeurIPS 2019, 2019	1052	2019
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	899	2020
A Syntactic Neural Model for General-Purpose Code Generation P Yin, G Neubig ACL 2017, 2017	790	2017
Towards a unified view of parameter-efficient transfer learning J He, C Zhou, X Ma, T Berg-Kirkpatrick, G Neubig ICLR 2022, 2022	786	2022
BARTScore: Evaluating Generated Text as Text Generation W Yuan, G Neubig, P Liu NeurIPS 2021, 2021	666	2021
A Systematic Evaluation of Large Language Models of Code FF Xu, U Alon, G Neubig, VJ Hellendoorn ICLR DL4C Workshop, 2022	558	2022
PAL: Program-aided Language Models L Gao, A Madaan, S Zhou, U Alon, P Liu, Y Yang, J Callan, G Neubig ICML 2023, 2022	540	2022
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data P Yin, G Neubig, W Yih, S Riedel ACL 2020, 2020	533	2020
Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017	451*	2017
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	411	2018
Weight Poisoning Attacks on Pre-trained Models K Kurita, P Michel, G Neubig ACL 2020, 2020	402	2020
Stress Test Evaluation for Natural Language Inference A Naik, A Ravichander, N Sadeh, C Rose, G Neubig COLING 2018, 2018	372	2018
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	370	2015
Competence-based Curriculum Learning for Neural Machine Translation EA Platanios, O Stretcu, G Neubig, B Poczos, TM Mitchell NAACL 2019, 2019	357	2019
Pointwise prediction for robust, adaptable Japanese morphological analysis G Neubig, Y Nakata, S Mori ACL 2011, 529-533, 2011	356	2011
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders J He, D Spokoyny, G Neubig, T Berg-Kirkpatrick ICLR 2019, 2019	339	2019
Controllable Invariance through Adversarial Feature Learning Q Xie, Z Dai, Y Du, E Hovy, G Neubig NIPS 2017, 2017	313	2017

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