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Results: 24
Number of items: 24
  • Open Access
    Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. In M. Bansal, & A. Villavicencio (Eds.), The 23rd Conference on Computational Natural Language Learning: CoNLL 2019 : proceedings of the conference : November 3-4, 2019, Hong Kong, China (pp. 1-11). The Association for Computational Linguistics. https://doi.org/10.18653/v1/K19-1001
  • Open Access
    Leonandya, R., Hupkes, D., Bruni, E., & Kruszewski, G. (2019). The Fast and the Flexible: training neural networks to learn to follow instructions from small data. In S. Dobnik, S. Chatzikyriakidis, & V. Demberg (Eds.), Proceedings of the 13th International Conference on Computational Semantics - Long Papers: IWCS 2019 : 23-27 May, 2019, University of Gothenburg, Gothenburg, Sweden (pp. 223-234). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-0419
  • Open Access
    Ulmer, D., Hupkes, D., & Bruni, E. (2019). Assessing incrementality in sequence-to-sequence models. In I. Augenstein, S. Gella, S. Ruder, K. Kann, B. Can, J. Welbl, A. Conneau, X. Ren, & M. Rei (Eds.), The 4th Workshop on Representation Learning for NLP (RepL4NLP-2019): ACL 2019 : proceedings of the workshop : August 2, 2019, Florence, Italy (pp. 209–217). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4324
  • Open Access
    Baan, J., Leible, J., Nikolaus, M., Rau, D., Ulmer, D., Baumgärtner, T., Hupkes, D., & Bruni, E. (2019). On the Realization of Compositionality in Neural Networks. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: ACL 2019 : proceedings of the Second Workshop : August 1, 2019, Florence, Italy (pp. 127-137). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4814
  • Open Access
    Lakretz, Y., Kruszewski, G., Desbordes, T., Hupkes, D., Dehaene, S., & Baroni, M. (2019). The emergence of number and syntax units in LSTM language models. In J. Burstein, C. Doran, & T. Solorio (Eds.), The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL HLT 2019 : proceedings of the conference : June 2-June 7, 2019 (Vol. 1, pp. 11-20). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1002
  • Zuidema, W., Hupkes, D., Wiggins, G. A., Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in Music, Language, and Animal Song. In H. Honing (Ed.), The Origins of Musicality (pp. 253-286). MIT Press. http://cognet.mit.edu/pdfviewer/book/9780262344548/chap11
  • Open Access
    Hupkes, D., Bouwmeester, S., & Fernández, R. (2018). Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), The 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: EMNLP 2018 : proceedings of the First Workshop : November 1, 2018, Brussels, Belgium (pp. 165–174). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5419
  • Open Access
    Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907-926. https://doi.org/10.1613/jair.1.11196
  • Open Access
    Jumelet, J., & Hupkes, D. (2018). Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), The 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: EMNLP 2018 : proceedings of the First Workshop : November 1, 2018, Brussels, Belgium (pp. 222-231). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5424
  • Open Access
    Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), The 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: EMNLP 2018 : proceedings of the First Workshop : November 1, 2018, Brussels, Belgium (pp. 240–248). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5426
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