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Results: 18
Number of items: 18
  • Open Access
    Patil, A., Jumelet, J., Chiu, Y. Y., Lapastora, A., Shen, P., Wang, L., Willrich, C., & Steinert-Threlkeld, S. (2024). Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence. Transactions of the Association for Computational Linguistics, 12, 1597-1615. https://doi.org/10.1162/tacl_a_00720
  • Open Access
    van de Pol, I., Lodder, P., van Maanen, L., Steinert-Threlkeld, S., & Szymanik, J. (2023). Quantifiers satisfying semantic universals have shorter minimal description length. Cognition, 232, Article 105150. https://doi.org/10.1016/j.cognition.2022.105150
  • Open Access
    Hawke, P., & Steinert-Threlkeld, S. (2021). Semantic Expressivism for Epistemic Modals. Linguistics and Philosophy, 44(2), 475–511. https://doi.org/10.1007/s10988-020-09295-7
  • Open Access
    Carcassi, F., Steinert-Threlkeld, S., & Szymanik, J. (2021). Monotone Quantifiers Emerge via Iterated Learning. Cognitive Science, 45(8), Article e13027. https://doi.org/10.1111/cogs.13027
  • Open Access
    Jumelet, J., Denić, M., Szymanik, J., Hupkes, D., & Steinert-Threlkeld, S. (2021). Language models use monotonicity to assess NPI licensing. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021: Findings of ACL: ACL-IJCNLP 2021 : August 1-6, 2021 (pp. 4958–4969). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.439
  • Open Access
    van de Pol, I., Lodder, P., van Maanen, L., Steinert-Threlkeld, S., & Szymanik, J. (2021). Quantifiers satisfying semantic universals are simpler. In 43rd Annual Meeting of the Cognitive Science Society (CogSci 2021): Comparative Cognition Animal Minds : Vienna, Austria, 26-29 July 2021 (Vol. 2, pp. 756-762). (Proceedings of the Annual Meeting of the Cognitive Science Society; Vol. 43). Cognitive Science Society. https://escholarship.org/uc/item/1vm445rp
  • Steinert-Threlkeld, S., & Szymanik, J. (2020). Ease of learning explains semantic universals. Cognition, 195, Article 104076. https://doi.org/10.1016/j.cognition.2019.104076
  • Open Access
    Ramotowska, S., Steinert-Threlkeld, S., Van Maanen, L., & Szymanik, J. (2020). Most, but not more than half, is proportion-dependent and sensitive to individual differences. In M. Franke, N. Kompa, M. Liu, J. L. Mueller, & J. Schwab (Eds.), Proceedings of Sinn und Bedeutung 24 (Vol. 2, pp. 165–182). Osnabrück University. https://doi.org/10.18148/sub/2020.v24i2.891
  • Open Access
    Steinert-Threlkeld, S. (2020). An Explanation of the Veridical Uniformity Universal. Journal of Semantics, 37(1), 129-144. https://doi.org/10.1093/jos/ffz019
  • Open Access
    Denić, M., Steinert-Threlkeld, S., & Szymanik, J. (2020). Complexity/informativeness trade-off in the domain of indefinite pronouns. Proceedings from Semantics and Linguistic Theory, 30, 166-184. https://doi.org/10.3765/salt.v30i0.4811
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