Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 8
Number of items: 8
  • Open Access
    Baan, J., Fernández, R., Plank, B., & Aziz, W. (2024). Interpreting Predictive Probabilities: Model Confidence or Human Label Variation? 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. 2, pp. 268-277). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-short.24
  • 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
  • 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
  • Open Access
    Baan, J., Aziz, W., Plank, B., & Fernández, R. (2022). Stop Measuring Calibration When Humans Disagree. 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. 1892–1915). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.124
  • Open Access
    Baan, J., ter Hoeve, M., van der Wees, M., Schuth, A., & de Rijke, M. (2019). Understanding Multi-Head Attention in Abstractive Summarization. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1911.03898
  • Open Access
    Olteanu, A., Garcia-Gathright, J., de Rijke, M., Ekstrand, M. D., Roegiest, A., Lipani, A., Beutel, A., Lucic, A., Stoica, A.-A., Das, A., Biega, A., Voorn, B., Hauff, C., Spina, D., Lewis, D., Oard, D. W., Yilmaz, E., Hasibi, F., Kazai, G., ... Kamishima, T. (2019). FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval. SIGIR Forum, 53(2), 20-43. http://sigir.org/wp-content/uploads/2019/december/p020.pdf
  • Open Access
    Baan, J., ter Hoeve, M., van der Wees, M., Schuth, A., & de Rijke, M. (2019). Do Transformer Attention Heads Provide Transparency in Abstractive Summarization? In Proceedings of FACTS-IR 2019 ArXiv. https://arxiv.org/abs/1907.00570
  • Open Access
    Baan, J., Leible, J., Nikolaus, M., Rau, D., Ulmer, D., Baumgärtner, T., Hupkes, D., & Bruni, E. (2019). On the Realization of Compositionality in Neural Networks. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: ACL 2019 : proceedings of the Second Workshop : August 1, 2019, Florence, Italy (pp. 127-137). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4814
Page of