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Results: 108
Number of items: 108
  • Open Access
    de Heer Kloots, M., Boersma, P., & Zuidema, W. (2026). Linguists should learn to love speech-based deep learning models. Behavioral and Brain Sciences, 49, Article e206. https://doi.org/10.1017/S0140525X26104713, https://doi.org/10.48550/arXiv.2512.14506
  • Open Access
    Sauter, A., Zuidema, W., & de Heer Kloots, M. (2026). The Curious Case of Visual Grounding: Different Effects for Speech-and Text-Based Language Encoders. In ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): proceedings : 4-8 May 2026, Barcelona, Spain (pp. 17917-17921). IEEE. https://doi.org/10.48550/arXiv.2509.15837, https://doi.org/10.1109/ICASSP55912.2026.11463880
  • Open Access
    van der Wal, O. D. (2026). Taking a step back: Measuring and mitigating bias in language models. [Thesis, fully internal, Universiteit van Amsterdam].
  • de Heer Kloots, M., Mohebbi, H., Pouw, C., Shen, G., Zuidema, W., & Bentum, M. (2025, May 29). SSL-NL dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15548946
  • Suijkerbuijk, M., Prins, Z., de Heer Kloots, M. L. S., Zuidema, W. H., & Frank, S. (2025). BLiMP-NL: The Benchmark of Linguistic Minimal Pairs for Dutch [Data set]. Radboud Universiteit. https://doi.org/10.34973/tj4p-y007
  • Open Access
    Suijkerbuijk, M., Prins, Z., de Heer Kloots, M., Zuidema, W., & Frank, S. L. (2025). BLiMP-NL: A Corpus of Dutch Minimal Pairs and Acceptability Judgments for Language Model Evaluation. Computational Linguistics, 51(4), 1267-1301. https://doi.org/10.31234/osf.io/mhjbx_v2, https://doi.org/10.1162/COLI_a_00559
  • Open Access
    Pouw, C., Alishahi, A., & Zuidema, W. (2025). A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivity. In G. Boleda, & M. Roth (Eds.), The 29th Conference on Computational Natural Language Learning (CoNLL 2025) : Proceedings of the Conference: CoNLL 2025 : July 31-August 1, 2025 (pp. 126-140). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.conll-1.9
  • Open Access
    de Heer Kloots, M., Mohebbi, H., Pouw, C., Shen, G., Zuidema, W., & Bentum, M. (2025). What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 26, 256-260. https://doi.org/10.48550/arXiv.2506.00981, https://doi.org/10.21437/Interspeech.2025-1526
  • Open Access
    Fresen, A. J., Choenni, R., Heilbron, M., Zuidema, W., & de Heer Kloots, M. (2024). Language Models That Accurately Represent Syntactic Structure Exhibit Higher Representational Similarity To Brain Activity. In L. Samuelson, S. Frank, M. Toneva, A. Mackey, & E. Hazeltine (Eds.), 46th Annual Meeting of the Cognitive Science Society (CogSci 2024): Dynamics of Cognition : Rotterdam, the Netherlands, 24-27 July 2024 (Vol. 2, pp. 675-683). (Proceedings of the Annual Meeting of the Cognitive Science Society; Vol. 46). Cognitive Science Society. https://escholarship.org/uc/item/1fp7m6nf
  • Open Access
    van Sprang, A., Acar, E., & Zuidema, W. (2024). Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2410.06070
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