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Results: 14
Number of items: 14
  • Open Access
    Johnson, T., ter Veen, M., Choenni, R., van der Maas, H. L. J., Shutova, E., & Stevenson, C. E. (2025). Do large language models solve verbal analogies like children do? 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. 627-639). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2310.20384, https://doi.org/10.18653/v1/2025.conll-1.40
  • Open Access
    Choenni, R. M. V. (2025). Multilinguality and multiculturalism: Towards effective and inclusive neural language models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Rajaee, S., Choenni, R., Shutova, E., & Monz, C. (2025). An Empirical Analysis of Machine Translation for Expanding Multilingual Benchmarks. In B. Haddow, T. Kocmi, P. Koehn, & C. Monz (Eds.), Tenth Conference on Machine Translation : Proceedings of the Conference: WMT 2025 : November 8-9, 2025 (pp. 1-30). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.wmt-1.1
  • Open Access
    Choenni, R., Lauscher, A., & Shutova, E. (2024). The Echoes of Multilinguality: Tracing Cultural Value Shifts during Language Model Fine-tuning. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) : proceedings of the conference: ACL 2024 : August 11-16, 2024 (Vol. 1, pp. 15042-15058). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.803
  • Open Access
    Choenni, R., Shutova, E., & Garrette, D. (2024). Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings : June 16-21, 2024 (pp. 287-301). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-naacl.21
  • Open Access
    Tong, X., Choenni, R., Lewis, M., & Shutova, E. (2024). Metaphor Understanding Challenge Dataset for LLMs. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) : proceedings of the conference: ACL 2024 : August 11-16, 2024 (Vol. 1, pp. 3517-3536). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.193
  • Open Access
    Tong, X., Choenni, R., Lewis, M., & Shutova, E. (2024). Metaphor Understanding Challenge Dataset for LLMs. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2403.11810
  • 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
    Choenni, R., Garrette, D., & Shutova, E. (2023). How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 13244-13257). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.818
  • Open Access
    Choenni, R., Garrette, D., & Shutova, E. (2023). Cross-Lingual Transfer with Language-Specific Subnetworks for Low-Resource Dependency Parsing. Computational Linguistics, 49(3), 613-641. https://doi.org/10.1162/coli_a_00482
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