Hawashin, H., Abbaszadeh, M., Joseph, N., Pearson, B., Lewis, M., & Sadrzadeh, M. (2025, November). Compositional Concept Generalization with Variational Quantum Circuits. In 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI) (pp. 34-40). IEEE.
Pearson, B., Boulbarss, B., Wray, M., & Lewis, M. (2025, November). Evaluating compositional generalisation in vlms and diffusion models. In Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (* SEM 2025) (pp. 122-133).
Guo, Z., Xue, C., Xu, Z., Bo, H., Ye, Y., Pierrehumbert, J. B., & Lewis, M. (2025, September). Quantifying Compositionality of Classic and State-of-the-Art Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 22130-22146).
Vegner, I., De Souza, S., Forch, V., Lewis, M., & Doumas, L. A. (2025, July). Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 31842-31856).
Owers, J., Shutova, E., & Lewis, M (2024). Density Matrices for Metaphor Understanding. Quantum Physics and Logic 2024 EPTCS 406, 197.
Lewis, M., Nayak, N., Yu, P., Merullo, J., Yu, Q., Bach, S., & Pavlick, E. (2024, March). Does CLIP Bind Concepts? Probing Compositionality in Large Image Models. In Findings of the Association for Computational Linguistics: EACL 2024 (pp. 1487-1500).
Lewis, M. (2023). Compositional Vector Semantics in Spiking Neural Networks. In Trends and Challenges in Cognitive Modeling: An Interdisciplinary Approach Towards Thinking, Memory, and Decision-Making Simulations (pp. 131-146). Cham: Springer International Publishing.
De las Cuevas, G., Klingler, A., Lewis, M., & Netzer, T. (2021). Cats Climb Entails Mammals Move: Preserving Hyponymy in Compositional Distributional Semantics. Journal of Cognitive Science, 22(3), 311-353.
Meyer, F., & Lewis, M. (2020, November). Modelling lexical ambiguity with density matrices. In Proceedings of the 24th Conference on Computational Natural Language Learning (pp. 276-290).
Lewis, M. (2020). Towards Logical Negation in Compositional Distributional Semantics. Journal of Applied Logics, 2631(5), 771.
Lewis, M. (2019, September). Compositional hyponymy with positive operators. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (pp. 638-647).
Lewis, M., (2018). Compositionality for Recursive Neural Networks Journal of Applied Logics, Vol 6, Issue 4, 709