Cikkek nyilvánosan hozzáférhető megbízással - Chris DyerTovábbi információ
Valahol hozzáférhető: 18
Finding function in form: Compositional character models for open vocabulary word representation
W Ling, T Luís, L Marujo, RF Astudillo, S Amir, C Dyer, AW Black, ...
arXiv preprint arXiv:1508.02096, 2015
Megbízások: Fundação para a Ciência e a Tecnologia, Portugal
Localizing syntactic predictions using recurrent neural network grammars
JR Brennan, C Dyer, A Kuncoro, JT Hale
Neuropsychologia 146, 107479, 2020
Megbízások: US National Science Foundation
Two/too simple adaptations of word2vec for syntax problems
W Ling, C Dyer, AW Black, I Trancoso
Proceedings of the 2015 conference of the North American chapter of the …, 2015
Megbízások: Fundação para a Ciência e a Tecnologia, Portugal
Many languages, one parser
W Ammar, G Mulcaire, M Ballesteros, C Dyer, NA Smith
Transactions of the Association for Computational Linguistics 4, 431-444, 2016
Megbízások: European Commission
Attention-based multimodal neural machine translation
PY Huang, F Liu, SR Shiang, J Oh, C Dyer
Proceedings of the First Conference on Machine Translation: Volume 2, Shared …, 2016
Megbízások: US National Science Foundation
Not all contexts are created equal: Better word representations with variable attention
W Ling, Y Tsvetkov, S Amir, R Fermandez, C Dyer, AW Black, I Trancoso, ...
Proceedings of the 2015 conference on empirical methods in natural language …, 2015
Megbízások: Fundação para a Ciência e a Tecnologia, Portugal
Automatic keyword extraction on twitter
L Marujo, W Ling, I Trancoso, C Dyer, AW Black, A Gershman, ...
Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015
Megbízások: Fundação para a Ciência e a Tecnologia, Portugal
Joint feature selection in distributed stochastic learning for large-scale discriminative training in SMT
P Simianer, S Riezler, C Dyer
Proceedings of the 50th Annual Meeting of the Association for Computational …, 2012
Megbízások: German Research Foundation
Machine learning for ancient languages: A survey
T Sommerschield, Y Assael, J Pavlopoulos, V Stefanak, A Senior, C Dyer, ...
Computational Linguistics 49 (3), 703-747, 2023
Megbízások: European Commission
Cross-lingual bridges with models of lexical borrowing
Y Tsvetkov, C Dyer
Journal of Artificial Intelligence Research 55, 63-93, 2016
Megbízások: US National Science Foundation
A continuous relaxation of beam search for end-to-end training of neural sequence models
K Goyal, G Neubig, C Dyer, T Berg-Kirkpatrick
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
Megbízások: US National Science Foundation
Using morphological knowledge in open-vocabulary neural language models
A Matthews, G Neubig, C Dyer
Proceedings of the 2018 Conference of the North American Chapter of the …, 2018
Megbízások: US Department of Defense
Syntactic structure distillation pretraining for bidirectional encoders
A Kuncoro, L Kong, D Fried, D Yogatama, L Rimell, C Dyer, P Blunsom
Transactions of the Association for Computational Linguistics 8, 776-794, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Greedy transition-based dependency parsing with stack lstms
M Ballesteros, C Dyer, Y Goldberg, NA Smith
Computational Linguistics 43 (2), 311-347, 2017
Megbízások: US National Science Foundation, US Department of Defense, European Commission
Transition-based dependency parsing with heuristic backtracking
J Buckman, M Ballesteros, C Dyer
Proceedings of the 2016 Conference on empirical methods in natural language …, 2016
Megbízások: European Commission
Synthesizing compound words for machine translation
A Matthews, E Schlinger, A Lavie, C Dyer
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
Megbízások: US National Science Foundation
Text genre and training data size in human-like parsing
J Hale, A Kuncoro, K Hall, C Dyer, J Brennan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
Megbízások: US National Science Foundation
Comparing top-down and bottom-up neural generative dependency models
A Matthews, G Neubig, C Dyer
Proceedings of the 23rd Conference on Computational Natural Language …, 2019
Megbízások: US Department of Defense
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