Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 16
Number of items: 16
  • Open Access
    Klamra, C., Keur, F., & Alhama, R. G. (2025). Noise May Drown Out Words but Foster Compositionality: The Advantage of the Erasure and Deletion Noisy Channels on Emergent Communication. In K. Inui, S. Sakti, H. Wang, D. F. Wong, P. Bhattacharyya, B. Banerjee, A. Ekbal, T. Chakraborty, & D. P. Singh (Eds.), The 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: proceedings of the conference : IJCNLP-AACL 2025 : December 20-24, 2025 (Vol. 1, pp. 3141-3166). Association for Computational Linguistics. https://aclanthology.org/2025.ijcnlp-long.168/
  • Open Access
    Pestel, J., Bloem, J., & Alhama, R. G. (2025). Evaluating Dutch Speakers and Large Language Models on Standard Dutch: a grammatical Challenge Set based on the Algemene Nederlandse Spraakkunst. Computational Linguistics in the Netherlands Journal, 14, 555-582. https://www.clinjournal.org/clinj/article/view/216
  • Open Access
    Akkerman, D., Le, P., & Alhama, R. G. (2024). The Emergence of Compositional Languages in Multi-entity Referential Games: from Image to Graph Representations. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 18713-18723). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1042, https://doi.org/10.18653/v1/2024.emnlp-main.1042
  • Open Access
    Alhama, R. G., Foushee, R., Byrne, D., Ettinger, A., Alishahi, A., & Goldin-Meadow, S. (2024). Using computational modeling to validate the onset of productive determiner-noun combinations in English-learning children. Proceedings of the National Academy of Sciences, 121(50), Article e2316527121. https://doi.org/10.1073/pnas.2316527121
  • Open Access
    Delcaro, N., Onnis, L., & Alhama, R. G. (2024). Predict but Also Integrate: an Analysis of Sentence Processing Models for English and Hindi. In T. Kuribayashi, G. Rambelli, E. Takmaz, P. Wicke, & Y. Oseki (Eds.), The 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics : proceedings of the workshop: CMCL 2024 : August 15, 2024 (pp. 101-108). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.cmcl-1.9
  • Open Access
    Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2020). Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. Topics in Cognitive Science, 12(3), 925-941. https://doi.org/10.1111/tops.12474
  • Open Access
    Alhama, R. G., Siegelman, N., Frost, R., & Armstrong, B. C. (2019). The Role of Information in Visual Word Recognition: A Perceptually-Constrained Connectionist Account. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Creativity + cognition + computation: 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) : Montreal, Canada, 24-27 July 2019 (Vol. 1, pp. 83-89). Cognitive Science Society. https://cognitivesciencesociety.org/cogsci-2019/
  • Open Access
    Alhama, R. G., & Zuidema, W. (2019). A review of computational models of basic rule learning: The neural-symbolic debate and beyond. Psychonomic Bulletin and Review, 26(4), 1174-1194. https://doi.org/10.3758/s13423-019-01602-z
  • Open Access
    Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? Journal of Artificial Intelligence Research, 61, 927-946. https://doi.org/10.1613/jair.1.11197
  • Open Access
    Stanojević, M., & Alhama, R. G. (2017). Neural Discontinuous Constituency Parsing. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1666-1676). Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1174
Page 1 of 2