Luke Zettlemoyer
Luke Zettlemoyer
University of Washington; Facebook
Bestätigte E-Mail-Adresse bei cs.washington.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Deep contextualized word representations
ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, ...
arXiv preprint arXiv:1802.05365, 2018
71312018
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
3665*2019
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
8892011
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
868*2005
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
8492019
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
6612019
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
6512018
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
6342017
End-to-end neural coreference resolution
K Lee, L He, M Lewis, L Zettlemoyer
arXiv preprint arXiv:1707.07045, 2017
4992017
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
4592020
Online learning of relaxed CCG grammars for parsing to logical form
L Zettlemoyer, M Collins
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007
4352007
Weakly supervised learning of semantic parsers for mapping instructions to actions
Y Artzi, L Zettlemoyer
Transactions of the Association for Computational Linguistics 1, 49-62, 2013
4002013
Open question answering over curated and extracted knowledge bases
A Fader, L Zettlemoyer, O Etzioni
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
3912014
Learning to parse natural language commands to a robot control system
C Matuszek, E Herbst, L Zettlemoyer, D Fox
Experimental robotics, 403-415, 2013
3792013
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
3582017
Scaling semantic parsers with on-the-fly ontology matching
T Kwiatkowski, E Choi, Y Artzi, L Zettlemoyer
Proceedings of the 2013 conference on empirical methods in natural language …, 2013
3332013
Paraphrase-driven learning for open question answering
A Fader, L Zettlemoyer, O Etzioni
Proceedings of the 51st Annual Meeting of the Association for Computational …, 2013
3332013
Inducing probabilistic CCG grammars from logical form with higher-order unification
T Kwiatkowksi, L Zettlemoyer, S Goldwater, M Steedman
Proceedings of the 2010 conference on empirical methods in natural language …, 2010
3262010
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
2942018
A joint model of language and perception for grounded attribute learning
C Matuszek, N FitzGerald, L Zettlemoyer, L Bo, D Fox
arXiv preprint arXiv:1206.6423, 2012
2942012
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