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Results: 44
Number of items: 44
  • Open Access
    Wildenburg, F., Hanna, M., & Pezzelle, S. (2024). Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!. 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. 9598-9613). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2402.12486, https://doi.org/10.18653/v1/2024.findings-acl.572
  • 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
    Testoni, A., Sprott, J., & Pezzelle, S. (2024). Naming, Describing, and Quantifying Visual Objects in Humans and LLMs. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) : proceedings of the conference: ACL 2024 : August 11-16, 2024 (Vol. 2, pp. 547-557). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-short.50
  • Open Access
    Mehrparvar, B., & Pezzelle, S. (2024). Detecting and Translating Language Ambiguity with Multilingual LLMs. In J. Sälevä, & A. Owodunni (Eds.), The 4th Workshop on Multilingual Representation Learning : proceedings of the workshop: MRL 2024 : November 16, 2024 (pp. 310–323). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.mrl-1.26
  • Open Access
    Takmaz, E. K. (2024). Visual and linguistic processes in deep neural networks: A cognitive perspective. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    Takmaz, E., Brandizzi, N., Giulianelli, M., Pezzelle, S., & Fernández, R. (2023). Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the Association for Computational Linguistics: ACL 2023: July 9-14, 2023 (pp. 4198-4217). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.258
  • Open Access
    Surikuchi, A., Pezzelle, S., & Fernández, R. (2023). GROOViST: A Metric for Grounding Objects in Visual Storytelling. In H. Bouamor, 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. 3331-3339). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.202
  • Open Access
    Pezzelle, S., & Fernández, R. (2023). Semantic Adaptation to the Interpretation of Gradable Adjectives via Active Linguistic Interaction. Cognitive Science, 47(2), Article e13248. https://doi.org/10.1111/cogs.13248
  • Open Access
    Hanna, M., Belinkov, Y., & Pezzelle, S. (2023). When Language Models Fall in Love: Animacy Processing in Transformer Language Models. 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. 12120-12135). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.744
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