Boosting Neural Language Inference via Cascaded Interactive Reasoning

Natural language inference NLI aims to judge accurately the logical relationship between premises and hypotheses. Due to the diverse expressions, rich semantics, and complex contexts inherent in language, NLI remains challenging. While Transformer-based pre-trained models have brought notable improvements, existing methods usually leverage only the last-layer token representations, limiting their effectiveness for modeling complex semantic interactions. To address this, we propose a Cascaded Interactive Reasoning Network CIRN , which achieves deep semantic understanding by extracting multi-level semantic features within an interactive space. This hierarchical extraction mechanism simulates the progressive cognitive process from shallow to deep understanding, efficiently mining hidden semantic relationships between sentences. Extensive experiments across multiple benchmark datasets demonstrate consistent performance improvements over strong baseline methods.