2026-06-05原文2 min readUpdated: 2026-06-05

Multi-Granularity Reasoning for Natural Language Inference

This paper proposes a novel Multi-Granularity Reasoning Network (MGRN) for Natural Language Inference (NLI). It explicitly leverages hierarchical semantic features to mimic the human cognitive process from lexical matching to logical reasoning, capturing complex semantic relationships. Experiments show MGRN consistently outperforms strong baselines.

SourcearXiv Computational LinguisticsAuthor: Chunling Xi, Di Liang

[2606.05181] Multi-Granularity Reasoning for Natural Language Inference

[Submitted on 18 Apr 2026]

Title:Multi-Granularity Reasoning for Natural Language Inference

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Abstract:Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a premise and a hypothesis. Despite the remarkable success of transformer-based pre-trained models, most existing approaches primarily rely on the final-layer token representations, which are often insufficient for capturing the complex and hierarchical semantic interactions required for effective reasoning. In particular, fine-grained lexical cues, phrasal compositions, and higher-level contextual semantics are typically entangled or diluted in a single representation space. To address these limitations, we propose a novel \emph{Multi-Granularity Reasoning Network} (MGRN) that explicitly leverages hierarchical semantic features within an interactive reasoning space. The proposed framework mimics the human cognitive process of language understanding, which naturally progresses from shallow lexical matching to deeper semantic abstraction and logical reasoning. By integrating semantic information across multiple granularities in a progressive and structured manner, MGRN is able to uncover intricate semantic relationships underlying natural language expressions. Extensive experiments on multiple public benchmarks demonstrate that MGRN consistently outperforms strong baseline models, validating the effectiveness and robustness of the proposed approach.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.05181 [cs.CL]

(or arXiv:2606.05181v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.05181

arXiv-issued DOI via DataCite

Submission history

From: Di Liang [view email] [v1] Sat, 18 Apr 2026 16:19:32 UTC (4,803 KB)

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