@inproceedings{gupta-etal-2026-dual,
    title = "Dual-View Consistency Testing for Content-Invariant Multilingual Syllogistic Reasoning",
    author = "Gupta, Ishita  and
      Goyal, Dhruv  and
      Bedi, Jatin",
    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-1.224/",
    pages = "1765--1771",
    ISBN = "979-8-89176-414-9",
    abstract = "Team 0704mis addressed the SemEval-2026 Task 11 Subtask 3 by building a neuro-symbolic system designed for multilingual syllogistic validity classification across 12 typologically diverse languages. The process involves a neural parser that extracts logical forms from text, which are then validated by a symbolic verifier implementing the full set of 24 valid Aristotelian forms via a hash lookup.Our standout contribution is the dual-view consistency test: the system compares a ``native'' parse of the original text with a ``masked'' version where content terms are replaced by abstract symbols (X, Y, Z), only proceeding with high confidence if both views agree. By comparing how the model interprets the same logic in two different formats, the system can detect if the model{'}s reasoning changes when the context shifts from real-world objects to abstract symbols. The primary goal is to combat belief bias, the human-like tendency of LLMs to accept invalid arguments if the conclusion sounds true, or reject valid arguments if the conclusion sounds false. By enforcing this dual-view check, we found that symbol abstraction (View B) acts as a structural regularizer, forcing the model to ignore semantic interference and focus on the relationship between terms."
}