MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing
Vlad Andrei Negru, Robert Vacareanu, Camelia Lemnaru, Mihai Surdeanu, Rodica Potolea · 2025
We introduce MorphNLI, a modular stepby-step approach to natural language inference (NLI).When classifying the premisehypothesis 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.