Charles Torres

ML engineer and his owner
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Overview Publications Projects Experience

Publications

Papers

  • Torres, C. J., Xu, W., Li, Y., & Futrell, R. (2025). Creolization versus code-switching: An agent-based cognitive model for bilingual strategies in language contact. Cognitive Modeling and Computational Linguistics.

  • Torres, C. J., & Futrell, R. (2025). Clarifying orthography: Orthographic transparency as compressibility [Under Review]. https://arxiv.org/abs/2505.13657

  • Torres, C. J., Hanson, K., Graf, T., & Mayer, C. (2023). Modeling island effects with probabilistic tier-based strictly local grammars over trees. Proceedings of the Society for Computation in Linguistics, 6, 155–164.

  • Torres, C. J., & Futrell, R. (2023). Simpler neural networks prefer subregular languages. Findings of the Association for Computational Linguistics: EMNLP 2023, 1651–1661.

  • Dyer, W., Torres, C. J., Scontras, G., & Futrell, R. (2023). Evaluating a century of progress on the cognitive science of adjective ordering. Transactions of the Association for Computational Linguistics, 11.

Conference Presentations

  • Torres, C. J., & Morgan, E. (2026). Chunking explains frequency-dependent regularization. Human Sentence Processing.

  • Torres, C. J. (2026). Against cognitive transparency: Addressing gaps in linguistic nativism. Cluster on Language Research.

  • Torres, C. J., & Futrell, R. (2024). Measuring orthographic complexity. California Meeting on Psycholinguistics.

  • Torres, C. J., & Futrell, R. (2024). Compositionality and algorithmic complexity in neural networks. The Evolution of Language: Proceedings of the 15th International Conference (Evolang XV). https://evolang2024.github.io/proceedings/paper.html?nr=140

  • Torres, C. J., & Futrell, R. (2023). L0-regularization induces subregular biases in LSTMs.

  • Torres, C. J., & Futrell, R. (2023). Formal language and computational complexity: seeking explanations for subregular natural language patterns. Human Sentence Processing.

  • Zhao, Y., Torres, C., & Fedzechkina, M. (2021). Preferences for shorter dependencies in miniature language learning are modulated by the statistics of learners’ L1. Human Sentence Processing.

  • Fedzechkina, M., Torres, C., & Zhao, Y. (2020). Dependency length minimization: An abstract bias or an input-driven preference?. Architectures and Mechanisms for Language Processing.