Integrating Natural Language Processing for Sophisticated Semantic Parsing and Context Management in Dialogue Systems

Abstract

To achieve a logically rigorous and highly cohesive intelligent dialogue interaction, this paper introduces technological innovations in two key areas: semantic understanding and dialogue management. First, it proposes a method to enhance semantic representations of user intent and entity relationships by integrating pre-trained language models with personalized fine-tuning. Second, it constructs a context framework matrix and employs reinforcement learning strategies to ensure the consistency of multi-turn dialogues. Testing on a user voice query dataset demonstrates substantial improvements in critical quality metrics compared to Seq2Seq benchmarks. The results suggest that the combination of advanced semantic modeling and effective context tracking markedly improves the dialogue system's capabilities in understanding, reasoning, and generating coherent responses.