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Luke Zettlemoyer
Tytuł
Cytowane przez
Cytowane przez
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Roberta: A robustly optimized bert pretraining approach
Y Liu, M Ott, N Goyal, J Du, M Joshi, D Chen, O Levy, M Lewis, ...
arXiv preprint arXiv:1907.11692, 2019
22148*2019
Deep contextualized word representations
ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, ...
NAACL, 2018
14741*2018
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
M Lewis, Y Liu, N Goyal, M Ghazvininejad, A Mohamed, O Levy, ...
arXiv preprint arXiv:1910.13461, 2019
82802019
Unsupervised cross-lingual representation learning at scale
A Conneau, K Khandelwal, N Goyal, V Chaudhary, G Wenzek, F Guzmán, ...
arXiv preprint arXiv:1911.02116, 2019
48982019
Spanbert: Improving pre-training by representing and predicting spans
M Joshi, D Chen, Y Liu, DS Weld, L Zettlemoyer, O Levy
Transactions of the association for computational linguistics 8, 64-77, 2020
18982020
Opt: Open pre-trained transformer language models
S Zhang, S Roller, N Goyal, M Artetxe, M Chen, S Chen, C Dewan, ...
arXiv preprint arXiv:2205.01068, 2022
1799*2022
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
M Joshi, E Choi, DS Weld, L Zettlemoyer
arXiv preprint arXiv:1705.03551, 2017
16542017
Multilingual denoising pre-training for neural machine translation
Y Liu, J Gu, N Goyal, X Li, S Edunov, M Ghazvininejad, M Lewis, ...
Transactions of the Association for Computational Linguistics 8, 726-742, 2020
14072020
Allennlp: A deep semantic natural language processing platform
M Gardner, J Grus, M Neumann, O Tafjord, P Dasigi, N Liu, M Peters, ...
arXiv preprint arXiv:1803.07640, 2018
13212018
Knowledge-based weak supervision for information extraction of overlapping relations
R Hoffmann, C Zhang, X Ling, L Zettlemoyer, DS Weld
Proceedings of the 49th annual meeting of the association for computational …, 2011
11902011
Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
LS Zettlemoyer, M Collins
Conference on Uncertainty in Artificial Intelligence (UAI), 2005
1116*2005
End-to-end neural coreference resolution
K Lee, L He, M Lewis, L Zettlemoyer
arXiv preprint arXiv:1707.07045, 2017
10622017
QuAC: Question answering in context
E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer
arXiv preprint arXiv:1808.07036, 2018
7992018
Summarizing source code using a neural attention model
S Iyer, I Konstas, A Cheung, L Zettlemoyer
54th Annual Meeting of the Association for Computational Linguistics 2016 …, 2016
7622016
Adversarial example generation with syntactically controlled paraphrase networks
M Iyyer, J Wieting, K Gimpel, L Zettlemoyer
arXiv preprint arXiv:1804.06059, 2018
6862018
Rethinking the role of demonstrations: What makes in-context learning work?
S Min, X Lyu, A Holtzman, M Artetxe, M Lewis, H Hajishirzi, L Zettlemoyer
arXiv preprint arXiv:2202.12837, 2022
6372022
Toolformer: Language models can teach themselves to use tools
T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, E Hambro, ...
Advances in Neural Information Processing Systems 36, 2024
5872024
Alfred: A benchmark for interpreting grounded instructions for everyday tasks
M Shridhar, J Thomason, D Gordon, Y Bisk, W Han, R Mottaghi, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
5512020
Deep semantic role labeling: What works and what’s next
L He, K Lee, M Lewis, L Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017
5432017
Higher-order coreference resolution with coarse-to-fine inference
K Lee, L He, L Zettlemoyer
arXiv preprint arXiv:1804.05392, 2018
5392018
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Prace 1–20