@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://aclanthology.org/2025.findings-naacl.385/",
doi = "10.18653/v1/2025.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."
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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.</abstract>
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%0 Conference Proceedings
%T MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing
%A Negru, Vlad Andrei
%A Vacareanu, Robert
%A Lemnaru, Camelia
%A Surdeanu, Mihai
%A Potolea, Rodica
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F negru-etal-2025-morphnli
%X 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.
%R 10.18653/v1/2025.findings-naacl.385
%U https://aclanthology.org/2025.findings-naacl.385/
%U https://doi.org/10.18653/v1/2025.findings-naacl.385
%P 6938-6953
Markdown (Informal)
[MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing](https://aclanthology.org/2025.findings-naacl.385/) (Negru et al., Findings 2025)
ACL