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• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985222

  Sieker, J., & Zarrieß, S. (2023). When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle. In Y. Belinkov, S. Hao, J. Jumelet, N. Kim, A. McCarthy, H. Mohebbi, & Association for Computational Linguistics (Eds.), Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP (p. 180–198).

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• [3]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983376

  Sieker, J., Bott, O., Solstad, T., & Zarrieß, S. (2023). Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models. In C. M. Keet, H. - Y. Lee, S. Zarrieß, & Association for Computational Linguistics (Eds.), Proceedings of the 16th International Natural Language Generation Conference (p. 206–220).

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• [2]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968165

  Sieker, J., & Solstad, T. (2022). Projective variability of (semi)factive verbs in family of sentence contexts: A rating study. In M. Degano, T. Roberts, G. Sbardolini, M. Schouwstra, & Institute for Logic, Language and Computation (ILLC) (Eds.), Proceedings of the 23rd Amsterdam Colloquium (pp. 282-289). Amsterdam.

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• [1]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2965490

  Reiter, N., Sieker, J., Guhr, S., Gius, E., & Zarrieß, S. (2022). Exploring Text Recombination for Automatic Narrative Level Detection. Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), 3346-3353. European Language Resources Association (ELRA).

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