Design of an Intelligent Dialogue System Based on Natural Language Processing

Abstract

In order to achieve a logically rigorous and highly continuous intelligent dialogue interaction, this paper innovates in technology from two aspects: semantic understanding and dialogue management. Firstly, by combining pre-trained language models with personalized fine-tuning, a method for enhancing semantic representations of user intent and entity relationships is proposed. Secondly, a context framework matrix is constructed, and reinforcement learning strategies are applied to maintain the consistency of multi-turn dialogues. Testing on a user voice query dataset shows significant improvements in key quality metrics compared to Seq2Seq benchmarks. The results indicate that the combination of semantic modeling and context tracking can significantly enhance the overall capability of the dialogue system in understanding, reasoning, and generating coherent responses.