Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 15
Number of items: 15
  • Open Access
    Jumelet, J., Zuidema, W., & Sinclair, A. (2024). Do Language Models Exhibit Human-like Structural Priming Effects? In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024: ACL 2024 : August 11-16, 2024 (pp. 14727-14742). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.877
  • Open Access
    Sinclair, A. J., & Fernández, R. (2023). Alignment of code switching varies with proficiency in second language learning dialogue. System, 113, Article 102952. https://doi.org/10.1016/j.system.2022.102952
  • Open Access
    Molnar, A., Jumelet, J., Giulianelli, M., & Sinclair, A. (2023). Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue. In J. Jiang, D. Reitter, & S. Deng (Eds.), The 27th Conference on Computational Natural Language Learning: CoNLL 2023 : proceedings of the conference : December 6-7, 2023 (pp. 254–273). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.conll-1.18
  • Open Access
    Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., & Jin, Z. (2023). A taxonomy and review of generalization research in NLP. Nature Machine Intelligence, 5(10), 1161-1174. https://doi.org/10.48550/arXiv.2210.03050, https://doi.org/10.1038/S42256-023-00729-Y
  • Ryb, S., Giulianelli, M., Sinclair, A., & Fernández, R. (2022). AnaLog [Data set]. GitHub. https://github.com/dmg-illc/analog
  • Open Access
    Jansen, L., Laichter, Š. L., Sinclair, A., van der Goot, M. J., Fernández, R., & Pezzelle, S. (2022). Controllable Text Generation for All Ages: Evaluating a Plug-and-Play Approach to Age-Adapted Dialogue. In A. Bosselut, K. Chandu, K. Dhole, V. Gangal, S. Gehrmann, Y. Jernite, J. Novikova, & L. Perez-Beltrachini (Eds.), 2nd Workshop on Natural Language Generation, Evaluation and Metrics: GEM 2022 : proceedings of the workshop : December 7, 2022 (pp. 172-188). Association for Computational Linguistics. https://doi.org/https://aclanthology.org/2022.gem-1.14
  • Open Access
    Ryb, S., Giulianelli, M., Sinclair, A., & Fernández, R. (2022). AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models. In V. Nastase, E. Pavlick, M. T. Pilehvar, J. Camacho-Collados, & A. Raganato (Eds.), The 11th Joint Conference on Lexical and Computational Semantics: *SEM 2022 : proceedings of the conference : July 14-15, 2022 (pp. 55-68). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.starsem-1.5
  • Open Access
    Sinclair, A., Jumelet, J., Zuidema, W., & Fernández, R. (2022). Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations. Transactions of the Association of Computational Linguistics, 10, 1031–1050. https://doi.org/10.1162/tacl_a_00504
  • Open Access
    Giulianelli, M., Sinclair, A., & Fernández, R. (2022). Construction Repetition Reduces Information Rate in Dialogue. In Y. He, H. Ji, S. Li, Y. Liu, & C.-H. Chang (Eds.), The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: proceedings of the conference : AACL-IJCNLP 2022 : November 20-23, 2022 (Vol. 1, pp. 665-682). The Association for Computational Linguistics. https://aclanthology.org/2022.aacl-main.51
  • Open Access
    Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., & Jin, Z. (2022). State-of-the-art generalisation research in NLP: A taxonomy and review. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2210.03050
Page 1 of 2