@inproceedings{strohmaier-wimmer-2026-transformers,
    title = "Transformers Learning Contrafactives: The Importance of Data Distributions",
    author = "Strohmaier, David  and
      Wimmer, Simon",
    editor = "Bernard, Timoth{\'e}e  and
      Chersoni, Emmanuele  and
      Rambelli, Giulia",
    booktitle = "Proceedings of the Third Workshop on the Bridges and Gaps between Formal and Computational Linguistics ({B}ri{G}ap-3)",
    month = jul,
    year = "2026",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-brigap/2026.brigap-1.10/",
    pages = "94--120",
    ISBN = "XXX-X-XXXXXX-XX-X",
    abstract = "No natural language is known to have contrafactive attitude verbs, yet factives are common across natural languages. Several experiments by Strohmaier and Wimmer (2022; 2023; 2025) use transformers as model learners to investigate whether this asymmetry is due to a difference in how easy it is to learn contrafactives and factives. But they do not explore empirically-founded data distributions. We fill this gap, further improving the overall quality of training data distributions using linear programming.Our results confirm Strohmaier and Wimmer{'}s 2025 conclusion that there is no learnability difference in production, while establishing the impact of differences in data distributions."
}