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Results: 131
Number of items: 131
  • Open Access
    Surikuchi, A. K., Fernández, R., & Pezzelle, S. (2024). Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2024: EMNLP 2024 : November 12-16, 2024 (pp. 11597–11611). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.679
  • Open Access
    Luden, I., Giulianelli, M., & Fernández, R. (2024). Beyond Perplexity: Examining Temporal Generalization in Large Language Models via Definition Generation. Computational Linguistics in the Netherlands Journal, 13, 205–232. https://clinjournal.org/clinj/article/view/181
  • Open Access
    Ghaleb, E., Rasenberg, M., Pouw, W., Toni, I., Holler, J., Özyürek, A., & Fernández, R. (2024). Analysing Cross-Speaker Convergence through the Lens of Automatically Detected Shared Linguistic Constructions. In L. Samuelson, S. Frank, M. Toneva, A. Mackey, & E. Hazeltine (Eds.), 46th Annual Meeting of the Cognitive Science Society (CogSci 2024): Dynamics of Cognition : Rotterdam, the Netherlands, 24-27 July 2024 (Vol. 3, pp. 1717-1723). (Proceedings of the Annual Meeting of the Cognitive Science Society; Vol. 46). Cognitive Science Society. https://escholarship.org/uc/item/43h970fc
  • Open Access
    Takmaz, E. K. (2024). Visual and linguistic processes in deep neural networks: A cognitive perspective. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Testoni, A., & Fernández, R. (2024). Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions. In Y. Graham, & M. Purver (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics : Proceedings of the Conference: EACL 2024 : March 17-22, 2024 (Vol. 1, pp. 258-275). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-long.16
  • Open Access
    Qi, J., Sarti, G., Fernández, R., & Bisazza, A. (2024). Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 6037-6053). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.347
  • Giulianelli, M., Baan, J., Aziz, W., Fernández, R., & Plank, B. (2023, October 20). whatsnext-scores [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10025272
  • Giulianelli, M., Wallbridge, S., & Fernández, R. (2023, October 20). AltGen: 1.3M Plausible Alternatives From Neural Text Generators [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10006413
  • Chen, X., Fernández, R., & Pezzelle, S. (2023). The BLA Benchmark: Investigating Basic Language Abilities of Multimodal Models [Data set]. GitHub. https://github.com/shin-ee-chen/BLA
  • Open Access
    Giulianelli, M., Baan, J., Aziz, W., Fernández, R., & Plank, B. (2023). What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability. In H. Bouamar, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 14349–14371). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.887
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