@inproceedings{miyagawa-etal-2026-neural,
    title = "Neural Wani: Toward Accelerating the Automated Theorem Prover wani for Dependent Type Theory",
    author = "Miyagawa, Nanako  and
      Daido, Hinari  and
      Bekki, Daisuke",
    editor = "Bernard, Timoth{\'e}e  and
      Chersoni, Emmanuele  and
      Rambelli, Giulia",
    booktitle = "Proceedings of the Third Workshop on the Bridges and Gaps between Formal and Computational Linguistics ({B}ri{G}ap-3)",
    month = jul,
    year = "2026",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-brigap/2026.brigap-1.2/",
    pages = "12--21",
    ISBN = "XXX-X-XXXXXX-XX-X",
    abstract = "This paper proposes Neural Wani, an integration of a neural model into the automated theorem prover wani for Dependent Type Theory (DTT), aimed at accelerating proof search in natural language inference (NLI) pipelines. We implemented a lightweight LSTM-based model to predict the probability distribution of applicable inference rules and integrated it into wani{'}s backward inference process. Evaluation using the JSeM dataset demonstrates that Neural Wani achieves a 1.41x speedup compared to the standard non-neural baseline. Although slight overhead is observed in simpler proofs, our results indicate that neural-symbolic integration effectively guides search in complex DTT-based automated theorem proving."
}