@inproceedings{thorn-jakobsen-etal-2021-spurious,
    title = "Spurious Correlations in Cross-Topic Argument Mining",
    author = "Thorn Jakobsen, Terne Sasha  and
      Barrett, Maria  and
      S{\o}gaard, Anders",
    editor = "Ku, Lun-Wei  and
      Nastase, Vivi  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.starsem-1.25/",
    doi = "10.18653/v1/2021.starsem-1.25",
    pages = "263--277",
    abstract = "Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than merely relying on within-topic spurious correlations. We examine the effectiveness of this approach by analysing the output of single-task and multi-task models for cross-topic argument mining, through a combination of linear approximations of their decision boundaries, manual feature grouping, challenge examples, and ablations across the input vocabulary. Surprisingly, we show that cross-topic models still rely mostly on spurious correlations and only generalise within closely related topics, e.g., a model trained only on closed-class words and a few common open-class words outperforms a state-of-the-art cross-topic model on distant target topics."
}