Dieuwke Hupkes bio photo

Dieuwke Hupkes

Postdoc and scientific manager of the ELLIS Amsterdam unit, interested in structures in language

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Visiting address

Room F3.260
Science Park 904
1098 XH Amsterdam

Curriculum Vitae


Publications

Overview

You can also find me on Google Scholar.

In prep

  • Lakretz, Y., Hupkes, D., Vergallito, A., Marelli, M., Baroni, M. and Dehaene, S. Exploring processing of nested dependencies in neural-network language models and humans.
    [preprint]

2020

  • Luna R.D., Ponti E.M., Hupkes D., and Bruni E. Internal and External Pressures on Language Emergence: Least Effort, Object Constancy and Frequency. Accepted for EMNLP-findings
    [preprint]

  • van der Wal, O., de Boer, S., Bruni, E. and Hupkes, D. The grammar of emergent languages. Accepted at EMNLP 2020
    [paper] [source code]

  • Hupkes D., Dankers V., Mul M. and Bruni E. Compositionality decomposed: how do neural networks generalise? JAIR.
    [paper] [source code] [extended abstract (IJCAI)]

  • Dubois Y., Dagan G., Hupkes D., and Bruni E. Location Attention for Extrapolation to Longer Sequences. ACL2020.
    [paper]

2019

  • Jumelet J., Zuidema W. and Hupkes D. Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. CONLL 2019. Honourable mention.
    [paper] [source code]

  • Baan J., Leible J., Nikolaus M., Rau D., Ulmer D., Baumgärtner T., Hupkes D. and Bruni E. On the Realization of Compositionality in Neural Networks. BlackboxNLP, ACL 2019.
    [paper]

  • Ulmer D., Hupkes, D. and Bruni, E. Assessing incrementality in sequence-to-sequence models. Repl4NLP, ACL 2019.
    [paper]

  • Korrel K., Hupkes D., Dankers V., and Bruni E. Transcoding compositionally: using attention to find more generalizable solutions. BlackboxNLP, ACL 2019.
    [paper]

  • Lakretz Y., Kruszewski G., Desbordes T., Hupkes D., Dehaene S. and Baroni M. The emergence of number and syntax units in LSTM language models. NAACL 2019.
    [paper] [bibtex].

  • Hupkes D., Singh A.K., Korrel K., Kruszewski G. and, Bruni E. Learning compositionally through attentive guidance. CICLing 2019.
    [paper] [source code]

  • Leonandya R., Bruni E., Hupkes D. and Kruszewski G. The Fast and the Flexible: training neural networks to learn to follow instructions from small data. IWCS 2019.
    [paper]

2018

  • Hupkes D., Veldhoen S., and Zuidema W. (2018). Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. JAIR
    [paper] [bibtex] [demo] [extended abstract (IJCAI)]

  • Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D. and Zuidema, W. (2018). Under the hood: using diagnostic classifiers to investigate and improve how language models track agreement information.
    BlackboxNLP 2018, ACL. Best paper award.
    [paper] [bibtex].

  • Zuidema W., Hupkes D., Wiggins G., Scharf C. and Rohrmeier M. (2018). Formal models of Structure Building in Music, Language and Animal Song. In The Origins of Musicality
    [chapter]

  • Jumelet, J. and Hupkes, D. (2018). Do language models understand anything? On the ability of LSTMs to understand negative polarity items. BlackboxNLP 2018, ACL.
    [paper] [bibtex].

  • Hupkes, D., Bouwmeester, S. and Fernández, R. (2018). Analysing the potential of seq2seq models for incremental interpretation in task-oriented dialogue. BlackboxNLP 2018, ACL.
    [paper] [bibtex].

2017

  • Hupkes D. and Zuidema W. Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks. Interpreting, Explaining and Visualizing Deep Learning, NIPS2017.
    [paper] [poster] [source code]

2016

  • Veldhoen S., Hupkes D. and Zuidema W. (2016). Diagnostic classifiers: revealing how neural networks process hierarchical structure. CoCo, NIPS 2016.
    [paper] [bibtex] [poster] [source code] [demo]

  • Hupkes D. and Bod R. POS-tagging of Historical Dutch. LREC 2016.
    [bibtex] [paper] [source code]

Abstracts

  • Ponti E., Hupkes D. and Bruni E. (2019) The typology of emergent languages. Interaction and the Evolution of Linguistic Complexity
    [abstract]

  • Ulmer D., Hupkes D. and Bruni E. (2019) An incremental encoder for sequence-to-sequence modelling. CLIN29.
    [abstract]

  • Lakretz, Y., Kruszewski, G., Hupkes, D., Desbordes, T., Marti, S., Dehaene, S.and Baroni, M., (2018). The representation of syntactic structures in Long-Short Term Memory networks and humans. L2HM.
    [poster]

  • Zuidema, W., Hupkes, D., Abnar, S. (2018). Interpretable Machine Learning for Predicting Brain Activation in Language Processing. L2HM.
    [poster]

  • Leonandya, R., Kruszewski, G., Hupkes, D., Bruni, E. (2018). The Fast and the Flexible: pretraining neural networks to learn from small data. L2HM. [poster]

  • Nielsen A., Hupkes D., Kirby S. and Smith K. (2016). The Arbitrariness Of The Sign Revisited: The Role Of Phonological Similarity. EVOLANG11.
    [abstract]