@inproceedings{negru-etal-2025-morphnli,
    title = "{M}orph{NLI}: A Stepwise Approach to Natural Language Inference Using Text Morphing",
    author = "Negru, Vlad Andrei  and
      Vacareanu, Robert  and
      Lemnaru, Camelia  and
      Surdeanu, Mihai  and
      Potolea, Rodica",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.385/",
    pages = "6938--6953",
    ISBN = "979-8-89176-195-7",
    abstract = "We introduce MorphNLI, a modular step-by-step approach to natural language inference (NLI). When classifying the premise-hypothesis pairs into entailment, contradiction, neutral, we use a language model to generate the necessary edits to incrementally transform (i.e., morph) the premise into the hypothesis. Then, using an off-the-shelf NLI model we track how the entailment progresses with these atomic changes, aggregating these intermediate labels into a final output. We demonstrate the advantages of our proposed method particularly in realistic cross-domain settings, where our method always outperforms strong baselines with improvements up to 12.6{\%} (relative). Further, our proposed approach is explainable as the atomic edits can be used to understand the overall NLI label."
}