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arXiv · 2502.09567

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

Vlad Andrei Negru·Robert Vacareanu·Camelia Lemnaru·Mihai Surdeanu·Rodica Potolea
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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.

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Vlad Andrei Negru, Robert Vacareanu, Camelia Lemnaru, Mihai Surdeanu, Rodica Potolea. 2025-02-13. MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing. https://doi.org/10.18653/v1%2F2025.findings-naacl.385

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