Huguet Cabot, P.-L., Abadi, D., Fischer, A., Shutova, E. (2021) Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions.

In Merlo, P. Tiedemann, J. Tsarfaty, R. (Eds.), The 16th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2021 : proceedings of the conference : April 19-23, 2021 (pp 1921-1945). Association for Computational Linguistics.

Huguet Cabot, P.-L., Dankers, V., Abadi, D., Fischer, A., Shutova, E. (2020) The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse.

In Cohn, T. He, Y. Liu, Y. (Eds.), Findings of the Association for Computational Linguistics. Findings of ACL: EMNLP 2020: 16-20 November, 2020 (pp 4479-4488). The Association for Computational Linguistics.

Huizing, D., Schäfer, G. (2021) The Traveling k-Median Problem: Approximating Optimal Network Coverage.

In Koenemann, J. Peis, B. (Eds.), Approximation and Online Algorithms: 19th International Workshop, WAOA 2021, Lisbon, Portugal, September 6–10, 2021 : revised selected papers (pp 80-98) (Lecture Notes in Computer Science, Vol. 12982). Springer.

Huizing, D., van der Mei, R., Schäfer, G., Bhulai, S. (2022) The enriched median routing problem and its usefulness in practice.

Computers & Industrial Engineering, Vol. 168

Article | https://doi.org/10.1016/j.cie.2022.108063 | UvA-DARE

Hulvej Rod , N., Broadbent, A., Hulvej Rod , M., Russo, F., Arah , O.A., Stronks , K. (2023) Complexity in epidemiology and public health. Studying and acting on complex health problems through a mix of epidemiological methods and dat.

Epidemiology

Article | UvA-DARE

Hupkes, D., Bod, R. (2015) Methods for Part-of-Speech Tagging 17th-Century Dutch.

Poster | UvA-DARE

Hupkes, D., Bod, R. (2015) Using Parallel Data to Improve Part-of-speech Tagging of 17th Century Dutch.

Abstract | UvA-DARE

Hupkes, D., Bod, R. (2016) POS-tagging of Historical Dutch.

In Calzolari, N. Choukri, K. Declerck, T. Goggi, S. Grobelnik, M. Maegaard, B. Mariani, J. Mazo, H. Moreno, A. Odijk, J. Piperidis, S. (Eds.), LREC 2016 : Tenth International Conference on Language Resources and Evaluation: May 23-28, 2016, Grand Hotel Bernardin Conference Center, Portorož, Slovenia (pp 77-82). European Language Resources Association (ELRA).

Hupkes, D., Bouwmeester, S., Fernández, R. (2018) Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue.

In Linzen, T. Chrupała, G. Alishahi, A. (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.

Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., Jin, Z. (2022) State-of-the-art generalisation research in NLP: A taxonomy and review.

ArXiv.

Preprint | https://doi.org/10.48550/arXiv.2210.03050 | UvA-DARE

Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., Jin, Z. (2023) A taxonomy and review of generalization research in NLP.

Nature Machine Intelligence, Vol. 5 (pp 1161-1174)

Article | https://doi.org/10.48550/arXiv.2210.03050 | UvA-DARE

Hupkes, D., Singh, Anand Kumar, Korrel, K., Kruszewski, G., Bruni, E. (2019) Learning compositionally through attentive guidance.

Poster | UvA-DARE

Hupkes, D., Veldhoen, S., Zuidema, W. (2017) Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure.

ArXiv.

Preprint | https://doi.org/10.48550/arXiv.1711.10203 | UvA-DARE

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, Vol. 61 (pp 907-926)

Article | https://doi.org/10.1613/jair.1.11196 | UvA-DARE

Hupkes, D., Zuidema, W. (2017) Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks.

Paper | http://www.interpretable-ml.org/nips2017workshop/papers/12.pdf | UvA-DARE

Hupkes, D. (2020) Hierarchy and interpretability in neural models of language processing.

Institute for Logic, Language and Computation.

Thesis, fully internal | UvA-DARE

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Scholarpedia Journal, Vol. 2

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