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Results: 21
Number of items: 21
  • Open Access
    Guo, Z., Xue, C., Xu, Z., Bo, H., Ye, Y., Pierrehumbert, J. B., & Lewis, M. (2025). Quantifying Compositionality of Classic and State-of-the-Art Embeddings. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), The 2025 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2025: EMNLP 2025 : November 4-9, 2025 (pp. 22130–22146). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-emnlp.1206
  • Open Access
    Lewis, M., & Mitchell, M. (2025). Evaluating the Robustness of Analogical Reasoning in GPT Models. Transactions on Machine Learning Research, 2025, Article 3684. https://doi.org/10.48550/arXiv.2411.14215
  • Open Access
    Hawashin, H., Abbaszadeh, M., Joseph, N., Pearson, B., Lewis, M., & Sadrzadeh, M. (2025). Compositional Concept Generalization with Variational Quantum Circuits. In 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI): QAI 2025 : 2-5 November 2025, Napoli, Italy : proceedings (pp. 34-40). IEEE Computer Society. https://doi.org/10.1109/QAI63978.2025.00013
  • Open Access
    Vegner, I., de Souza, S., Forch, V., Lewis, M., & Doumas, L. A. A. (2025). Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) : proceedings of the conference: ACL 2025 : July 27-August 1, 2025 (Vol. 1, pp. 31842–31856). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-long.1537
  • Open Access
    Pearson, B., Boulbarss, B., Wray, M., & Lewis, M. (2025). Evaluating Compositional Generalisation in VLMs and Diffusion Models. In L. Frermann, & M. Stevenson (Eds.), The 14th Joint Conference on Lexical and Computational Semantics : proceedings of the conference (*SEM 2025): StarSEM 2025 : November 8-9, 2025 (pp. 122–133). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.starsem-1.9
  • 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
    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
    Srivastava, A., Siro, C., Shutova, E., Jumelet, J., ter Hoeve, M., Giulianelli, M., Lewis, M., Schubert, M., Tong, X., & BIG-bench authors (2022). Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2206.04615
  • Open Access
    Tong, X., Shutova, E., & Lewis, M. (2021). Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021 (pp. 4673-4686). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.372
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