@inproceedings{francies-etal-2026-reglat,
    title = "{REGLAT} at {S}em{E}val-2026 Task 12: Multi-Strategy Ensemble Reasoning for Event Causality Identification",
    author = "Francies, Mariam  and
      Ashraf, Nsrin  and
      Fetouh, Ahmed  and
      Khalil, Asad  and
      Nayel, Hamada",
    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-1.412/",
    pages = "3310--3315",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the multi-strategy ensemble approach that has been used to develop the model submitted to the Abductive Event Reasoning shared task. The proposed model combines semantic similarity, causal pattern recognition, and Large Language Models (LLMs) to identify causal relationships between news events and their causes. Our system achieved competitive performance by integrating semantic embedding-based similarity, explicit causal pattern matching, keyword overlap analysis, temporal alignment scoring, and LLM-enhanced reasoning. Our system achieved accuracies of 65.4{\textbackslash}{\%} and 43.2{\textbackslash}{\%} on the development set using the LLM-enhanced configuration and the non-LLM ensemble, respectively. The final score using the test set on the leaderboard is 0.3."
}