@inproceedings{takahashi-2026-hidetsune-semeval,
    title = "Hidetsune at {S}em{E}val-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity Classification",
    author = "Takahashi, Hidetsune",
    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-1.28/",
    pages = "193--199",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents a system that applies training and inference approaches for SemEval2026 Task 11 Subtask 1, which focuses on binary classification for content-independent validity reasoning in syllogistic inference. Building on fine-tuning of relatively standard language models, additional approaches were explored, including layer-wise deep supervision and in-context learning. Furthermore, models that had been previously trained on datasets related to logical reasoning were adapted to thetask through additional fine-tuning. Finally, refinement was performed at the inference stage by adjusting the softmax-based decision threshold of the selected model. The experimental results illustrate how model selection, training strategies, and threshold adjustment affect not only validity accuracy but also robustness against plausibility-driven bias, thereby contributing to improved logical integrity."
}