Dieuwke Hupkes
Dieuwke Hupkes
Research Scientist at Facebook AI Research
Verified email at - Homepage
Cited by
Cited by
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
Compositionality decomposed: How do neural networks generalise?
D Hupkes, V Dankers, M Mul, E Bruni
Journal of Artificial Intelligence Research 67, 757-795, 2020
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
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
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
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
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 213, 104699, 2021
Diagnostic Classifiers Revealing how Neural Networks Process Hierarchical Structure.
S Veldhoen, D Hupkes, WH Zuidema
CoCo@ NIPS, 69-77, 2016
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
The paradox of the compositionality of natural language: a neural machine translation case study
V Dankers, E Bruni, D Hupkes
arXiv preprint arXiv:2108.05885, 2021
Co-evolution of language and agents in referential games
G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:2001.03361, 2020
Transcoding compositionally: Using attention to find more generalizable solutions
K Korrel, D Hupkes, V Dankers, E Bruni
arXiv preprint arXiv:1906.01234, 2019
Learning compositionally through attentive guidance
D Hupkes, A Singh, K Korrel, G Kruszewski, E Bruni
arXiv preprint arXiv:1805.09657, 2018
State-of-the-art generalisation research in NLP: a taxonomy and review
D Hupkes, M Giulianelli, V Dankers, M Artetxe, Y Elazar, T Pimentel, ...
arXiv preprint arXiv:2210.03050, 2022
Location attention for extrapolation to longer sequences
Y Dubois, G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:1911.03872, 2019
Language models use monotonicity to assess NPI licensing
J Jumelet, M Denić, J Szymanik, D Hupkes, S Steinert-Threlkeld
arXiv preprint arXiv:2105.13818, 2021
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
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
Generalising to German plural noun classes, from the perspective of a recurrent neural network
V Dankers, A Langedijk, K McCurdy, A Williams, D Hupkes
Proceedings of the 25th Conference on Computational Natural Language …, 2021
POS-tagging of Historical Dutch
D Hupkes, R Bod
Proceedings of the Tenth International Conference on Language Resources and …, 2016
The system can't perform the operation now. Try again later.
Articles 1–20