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Results: 55
Number of items: 55
  • 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
    Zhang, Z., Zhang, Q., Gao, Z., Zhang, R., Shutova, E., Zhou, S., & Zhang, S. (2024). Gradient-based Parameter Selection for Efficient Fine-Tuning. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 28566-28577). IEEE Computer Society. https://doi.org/10.48550/arXiv.2312.10136, https://doi.org/10.1109/CVPR52733.2024.02699
  • Open Access
    Verhoeven, I., Mishra, P., Beloch, R., Yannakoudakis, H., & Shutova, E. (2024). A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings: Findings 2024 : June 16-21, 2024 (pp. 437-463). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-naacl.30
  • Open Access
    Leidinger, A., van Rooij, R., & Shutova, E. (2024). Are LLMs classical or nonmonotonic reasoners? Lessons from generics. 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. 2, pp. 558-573). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-short.51
  • Open Access
    Owers, J., Shutova, E., & Lewis, M. (2024). Density Matrices for Metaphor Understanding. Electronic Proceedings in Theoretical Computer Science, 406, 197-215. https://doi.org/10.4204/EPTCS.406.9
  • Open Access
    van der Heijden, N., Shutova, E., & Yannakoudakis, H. (2023). FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2023 : proceedings of the conference : May 2-6, 2023 (pp. 1187-1200). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.85
  • Open Access
    Tedeschi, S., Bos, J., Declerck, T., Hajič, J., Hershcovich, D., Hovy, E. H., Koller, A., Krek, S., Schockaert, S., Sennrich, R., Shutova, E., & Navigli, R. (2023). What's the Meaning of Superhuman Performance in Today's NLU? In A. Rogers, J. Boyd-Graper, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: ACL 2023 : Proceedings of the Conference : July 9-14, 2023 (Vol. 1, pp. 12471-12491). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.697
  • 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
    Zhang, Z., Yannakoudakis, H., Zhen, X., & Shutova, E. (2023). CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics : Findings of EACL 2023: EACL 2023 : May 2-6, 2023 (pp. 2586-2596). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-eacl.196
  • 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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