@inproceedings{kobayashi-etal-2026-neural,
    title = "Neural {DTS}: Integrating Hyperbolic Classifiers into Natural Language Inference Systems",
    author = "Kobayashi, Honoka  and
      Daido, Hinari  and
      Bekki, Daisuke",
    editor = "Yanaka, Hitomi  and
      Abzianidze, Lasha",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Meets Logic and Machine Learning ({NALOMA})",
    month = aug,
    year = "2026",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-naloma/2026.naloma-1.2/",
    pages = "9--18",
    ISBN = "979-8-89176-389-0",
    abstract = "Dependent Type Semantics (DTS) provides a highly rigorous framework for natural language inference (NLI), yet its scalability is severely bottlenecked by the need for manually created world knowledge. To overcome this knowledge acquisition bottleneck, we present a novel neuro-symbolic NLI system that integrates Hyperbolic Entailment Cones for automated conceptual hierarchy discovery. By exploiting the geometric properties of hyperbolic space, our model efficiently learns lexical entailment relations and dynamically injects them as logical axioms during the DTS proof-search process. Evaluations on our constructed diagnostic dataset show that our hybrid approach broadens the coverage of complex lexical variations and paraphrases without manual engineering."
}