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Results: 26
Number of items: 26
  • Ryb, S., Giulianelli, M., Sinclair, A., & Fernández, R. (2022). AnaLog [Data set]. GitHub. https://github.com/dmg-illc/analog
  • Open Access
    Giulianelli, M. (2022). Towards Pragmatic Production Strategies for Natural Language Generation. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: December 7-11, 2022, Abu Dhabi, United Arab Emirates (pp. 7978-7984). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.544
  • Open Access
    Giulianelli, M., Kutuzov, A., & Pivovarova, L. (2022). Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change. In N. Tahmasebi, S. Montariol, A. Kutuzov, S. Hengchen, H. Dubossarsky, & L. Borin (Eds.), 3rd International Workshop on Computational Approaches to Historical Language Change 2022: LChange 2022 : proceedings of the workshop : May 26-27, 2022 (pp. 54-67). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.lchange-1.6
  • 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
    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
    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
    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
  • Open Access
    Giulianelli, M., & Fernández, R. (2021). Analysing Human Strategies of Information Transmission as a Function of Discourse Context. In A. Bisazza, & O. Abend (Eds.), The 25th Conference on Computational Natural Language Learning: CoNLL 2021 : proceedings of the conference : November 10-11, 2021, online (pp. 647–660). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.conll-1.50
  • Open Access
    Giulianelli, M., Kutuzov, A., & Pivovarova, L. (2021). Grammatical Profiling for Semantic Change Detection. In A. Bisazza, & O. Abend (Eds.), The 25th Conference on Computational Natural Language Learning: CoNLL 2021 : proceedings of the conference : November 10-11, 2021, online (pp. 423–434). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.conll-1.33
  • Open Access
    Giulianelli, M., Sinclair, A., & Fernández, R. (2021). Is Information Density Uniform in Task-Oriented Dialogues? In M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 8271–8283). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.652
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