Multi-Granularity Reasoning for Natural Language Inference

Researchers have developed the Multi-Granularity Reasoning Network (MGRN), a new framework for natural language inference that explicitly leverages hierarchical semantic features across multiple levels of language understanding. The model, which mimics human cognitive progression from lexical matching to deeper logical reasoning, consistently outperformed strong baseline models on multiple public benchmarks. The approach addresses limitations in existing transformer-based models that rely primarily on final-layer token representations, which often fail to capture complex semantic interactions.

arXiv:2606.05181v1 Announce Type: new 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.