Boosting Neural Language Inference via Cascaded Interactive Reasoning

TL;DR

This paper introduces the Cascaded Interactive Reasoning Network (CIRN), a new model that enhances natural language inference by leveraging multi-level features and progressive reasoning across multiple network depths.

Contribution

It proposes a hierarchical, interactive architecture that mines semantic relationships at various layers, improving NLI performance over traditional terminal-layer methods.

Findings

CIRN outperforms baseline models on standard NLI datasets.

Multi-level feature integration improves semantic understanding.

Progressive reasoning enhances logical relationship detection.

Abstract

Natural Language Inference (NLI) focuses on ascertaining the logical relationship (entailment, contradiction, or neutral) between a given premise and hypothesis. This task presents significant challenges due to inherent linguistic features such as diverse phrasing, semantic complexity, and contextual nuances. While Pre-trained Language Models (PLMs) built upon the Transformer architecture have yielded substantial advancements in NLI, prevailing methods predominantly utilize representations from the terminal layer. This reliance on final-layer outputs may overlook valuable information encoded in intermediate layers, potentially limiting the capacity to model intricate semantic interactions effectively. Addressing this gap, we introduce the Cascaded Interactive Reasoning Network (CIRN), a novel architecture designed for deeper semantic comprehension in NLI. CIRN implements a hierarchical feature extraction strategy across multiple network depths, operating within an interactive space where cross-sentence information is continuously integrated. This mechanism aims to mimic a process of progressive reasoning, transitioning from surface-level feature matching to uncovering more profound logical and semantic connections between the premise and hypothesis. By systematically mining latent semantic relationships at various representational levels, CIRN facilitates a more thorough understanding of the input pair. Comprehensive evaluations conducted on several standard NLI benchmark datasets reveal consistent performance gains achieved by CIRN over competitive baseline approaches, demonstrating the efficacy of leveraging multi-level interactive features for complex relational reasoning.