Dieuwke Hupkes
Dieuwke Hupkes
Research Scientist at Facebook AI Research
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Title
Cited by
Cited by
Year
Visualisation and'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
D Hupkes, S Veldhoen, W Zuidema
Journal of Artificial Intelligence Research 61, 907-926, 2018
1292018
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
M Giulianelli, J Harding, F Mohnert, D Hupkes, W Zuidema
arXiv preprint arXiv:1808.08079, 2018
782018
The emergence of number and syntax units in LSTM language models
Y Lakretz, G Kruszewski, T Desbordes, D Hupkes, S Dehaene, M Baroni
arXiv preprint arXiv:1903.07435, 2019
612019
Compositionality decomposed: how do neural networks generalise?
D Hupkes, V Dankers, M Mul, E Bruni
Journal of Artificial Intelligence Research 67, 757-795, 2020
51*2020
Do language models understand anything? on the ability of lstms to understand negative polarity items
J Jumelet, D Hupkes
arXiv preprint arXiv:1808.10627, 2018
272018
Diagnostic classifiers revealing how neural networks process hierarchical structure
S Veldhoen, D Hupkes, WH Zuidema
CoCo@ NIPS, 2016
222016
Analysing neural language models: Contextual decomposition reveals default reasoning in number and gender assignment
J Jumelet, W Zuidema, D Hupkes
arXiv preprint arXiv:1909.08975, 2019
172019
Co-evolution of language and agents in referential games
G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:2001.03361, 2020
122020
Transcoding compositionally: using attention to find more generalizable solutions
K Korrel, D Hupkes, V Dankers, E Bruni
arXiv preprint arXiv:1906.01234, 2019
122019
Learning compositionally through attentive guidance
D Hupkes, A Singh, K Korrel, G Kruszewski, E Bruni
arXiv preprint arXiv:1805.09657, 2018
112018
On the realization of compositionality in neural networks
J Baan, J Leible, M Nikolaus, D Rau, D Ulmer, T Baumgärtner, D Hupkes, ...
arXiv preprint arXiv:1906.01634, 2019
92019
POS-tagging of Historical Dutch
D Hupkes, R Bod
Proceedings of the Tenth International Conference on Language Resources and …, 2016
72016
Masked language modeling and the distributional hypothesis: Order word matters pre-training for little
K Sinha, R Jia, D Hupkes, J Pineau, A Williams, D Kiela
arXiv preprint arXiv:2104.06644, 2021
52021
Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue
D Hupkes, S Bouwmeester, R Fernández
arXiv preprint arXiv:1808.09178, 2018
52018
Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks
D Hupkes, W Zuidema
Proceedings Workshop on Interpreting, Explaining and Visualizing Deep …, 2017
52017
Mechanisms for handling nested dependencies in neural-network language models and humans
Y Lakretz, D Hupkes, A Vergallito, M Marelli, M Baroni, S Dehaene
Cognition, 104699, 2021
32021
Internal and external pressures on language emergence: Least effort, object constancy and frequency
DR Luna, EM Ponti, D Hupkes, E Bruni
arXiv preprint arXiv:2004.03868, 2020
32020
Location attention for extrapolation to longer sequences
Y Dubois, G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:1911.03872, 2019
32019
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
R Leonandya, E Bruni, D Hupkes, G Kruszewski
arXiv preprint arXiv:1809.06194, 2018
32018
Exploring Processing of Nested Dependencies in Neural-Network Language Models and Humans
Y Lakretz, D Hupkes, A Vergallito, M Marelli, M Baroni, S Dehaene
arXiv preprint arXiv:2006.11098, 2020
22020
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