@inproceedings{huang-etal-2026-nash,
    title = "{NASH}: Numerically Aware Scoring Heuristic for Robust Semantic Similarity",
    author = "Huang, Yu-Shiang  and
      Lee, Yun-Yu  and
      Chou, Tzu-Hsin  and
      Lin, Che  and
      Wang, Chuan-Ju",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1119/",
    pages = "22303--22317",
    ISBN = "979-8-89176-395-1",
    abstract = "Numerical precision is critical in financial NLP, yet embedding-based semantic similarity metrics exhibit numerical blindness{---}failing to distinguish contradictory values within similar contexts. We introduce NASH (Numerically Aware Scoring Hueristic), a model-agnostic metric that decouples numerical verification from textual semantic evaluation through a three-stage pipeline: (1) modal separation via numeric masking, (2) dual-channel similarity estimation through masked-text similarity and context-aware numeric alignment, and (3) IDF-weighted aggregation. NASH functions as a drop-in enhancement to existing embedding-based metrics. Validated on our proposed NumFinE financial numerical evaluation benchmark and established semantic similarity datasets (STS-B, Financial-STS), NASH achieves substantial improvements in numerical sensitivity (up to +159.6{\%} on listwise ranking) while preserving general semantic performance, establishing a reliable standard for numeracy-aware evaluation."
}