@inproceedings{ksiezniak-2026-team,
    title = "Team ewelinaksiez at {S}em{E}val-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction",
    author = "Ksi{\k{e}}{\.z}niak, Ewelina",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.272/",
    pages = "2149--2154",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system for SemEval-2026 Task{\textasciitilde}11 Subtask{\textasciitilde}1 on content-independent syllogistic reasoning. The task evaluates whether language models can determine the formal validity of logical arguments independently of their semantic plausibility. To reduce content-driven biases, we propose a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words while preserving logical structure. Experiments based on fine-tuning microsoft/deberta-large-mnli show that abstraction-based augmentation reduces Content Effect and improves accuracy, leading to competitive performance on the official leaderboard. However, we observe substantial sensitivity to random initialization, suggesting that evaluation outcomes are partly influenced by stochastic factors. To better understand these effects, we conduct a layer-wise probing analysis using a Minimum Description Length framework, showing that the proposed approach decreases the accessibility of plausibility information in later transformer layers, indicating a shift toward more structure-oriented reasoning."
}