Enhancing Chatbot Response Relevance through Semantic Similarity Measures

Abstract

Semantic similarity measures have shown promise in enhancing natural language understanding by quantifying the likeness between textual elements. This paper investigates the application of semantic similarity measures to improve chatbot response relevance. By leveraging word embeddings and similarity metrics, this study aims to bridge the gap between simple keyword-based responses and contextually rich, relevant answers. The proposed approach integrates both traditional lexical measures and advanced vector-based embeddings to enhance user intent interpretation and ensure a more suitable response generation. By refining the candidate response set with these techniques, the chatbot is able to generate highly relevant and contextually accurate replies. The results demonstrate that combining deep learning-based embeddings with traditional semantic metrics can significantly enhance response
relevance, leading to more meaningful and human-like user interactions. Additionally, a feedback loop is incorporated to continuously learn from user interactions, further improving the quality of responses over time.