Contextual Understanding in Recurrent Neural Networks for Machine Comprehension of Complex Narratives

Abstract

This paper explores the integration of contextual information within recurrent neural network architectures for the machine comprehension of complex narratives. While recurrent models excel at capturing sequential dependencies, they often struggle to incorporate broader contextual factors when narrative structures become highly intricate and involve multiple interconnected events. To address this shortcoming, our approach extends classical architectures with dynamically updated context representations that adapt to evolving narrative states. By emphasizing nuanced linguistic cues and external knowledge, our framework aims to identify and connect dispersed details that are essential for understanding characters, motivations, causal links, and resolutions within lengthy texts. The resulting enriched representations are positioned to improve inference accuracy and interpretability, offering tangible insights into why specific narrative inferences are made. Our analysis delves into the mathematical foundations of state updates, explores how contextual gating mechanisms enhance narrative modeling, and demonstrates the system’s effectiveness in real-world scenarios. Empirical evaluations on diverse corpora highlight significant gains in benchmark metrics while maintaining computational efficiency. We additionally showcase interpretative techniques that reveal the internal reasoning processes of the system, thus providing a basis for trust and explainability. Ultimately, our findings show that recurrent architectures can benefit substantially from explicit context integration, paving the way for advanced, context-aware machine comprehension capabilities suited to complex narrative domains.