@inproceedings{osei-brefo-liang-2026-oseibrefo,
    title = "{O}sei{B}refo-Liang at {S}em{E}val-2026 Task 12: Hybrid Causal Knowledge Graphs and Neural-Symbolic Policy Optimisation for Abductive Event Reasoning",
    author = "Osei-Brefo, Emmanuel  and
      Liang, Huizhi(elly)",
    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-1.287/",
    pages = "2268--2274",
    ISBN = "979-8-89176-414-9",
    abstract = "Abductive Event Reasoning (AER) requires selecting plausible causal explanations for observed events from incomplete and noisy textual evidence. Unlike deductive reasoning, abductive inference proceeds from effects to candidate causes and is highly sensitive to distractor information and implicit multi-hop relationships. We present a hybrid neural-symbolic framework that models abductive reasoning as structured causal validation rather than unconstrained generation. Our framework integrates hybrid retrieval, micro-level evidence grounding, concept-level causal abstraction, reinforcement learning-based decision calibration, and structured Theorem-of-Thought verification. Experiments on SemEval-2026 Task 12 show that LLM reasoning constrained by structured causal graphs achieves the strongest development performance of 0.5288 and a leaderboard score of 0.61 on the test set, substantially outperforming symbolic-only and policy-only variants. These findings indicate that explicit causal modelling improves robustness in document-grounded abduction tasks."
}