MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing

• DOI: 10.48550/arxiv.2502.09567
• Publication Date: 2025-01-01
• Authors: Negru, Vlad Andrei; Vacareanu, Robert; Lemnaru, Camelia; Surdeanu, Mihai; Potolea, Rodica
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL)

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.<br/>16 pages, 11 figures, 8 tables. Accepted for NAACL 2025 Findings<br/>