Multi-Granularity Reasoning for Natural Language Inference

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

The Multi-Granularity Reasoning Network (MGRN) is a novel framework designed to enhance Natural Language Inference (NLI) by explicitly leveraging hierarchical semantic features within an interactive reasoning space. It addresses limitations of traditional transformer-based models that often rely solely on final-layer token representations, which can dilute or entangle fine-grained lexical cues and higher-level contextual semantics. MGRN mimics human cognitive processes, progressing from shallow lexical matching to deeper semantic abstraction. Extensive experiments on multiple public benchmarks, including SNLI and MultiNLI, demonstrate that MGRN consistently outperforms strong baselines, achieving average accuracy improvements of 0.8% with BERT-base and 0.7% with BERT-large, and notably surpassing RoBERTa-base by 1.5% and RoBERTa-large by 0.5%. Its robustness is also validated against various adversarial and perturbation settings.

Key takeaway

For NLP Engineers developing NLI or semantic matching systems, consider implementing multi-granularity reasoning. Your models can achieve superior accuracy and robustness by explicitly leveraging hierarchical semantic features across transformer layers. This approach, exemplified by MGRN's use of interaction matrices and DenseNet, helps overcome limitations of single-layer representations, leading to more reliable performance against diverse linguistic challenges and adversarial perturbations.

Key insights

MGRN enhances NLI by explicitly modeling hierarchical semantic interactions across transformer layers.

Principles

• Relying solely on final-layer representations obscures useful intermediate semantic signals.
• Multi-level semantic modeling captures both local and global differential information.
• Explicitly modeling fine-grained interaction patterns improves robustness against perturbations.

Method

MGRN constructs an interaction matrix from element-wise multiplication of sentence representations across BERT layers, stacks them, and processes with DenseNet for classification.

In practice

• Integrate multi-layer interaction matrices for richer feature information.
• Use DenseNet for high-level feature extraction from stacked interaction matrices.
• Frame paraphrase identification as a binary NLI task for general applicability.

Topics

• Natural Language Inference
• Multi-Granularity Reasoning Network
• Transformer Models
• Semantic Matching
• Model Robustness
• DenseNet

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.