Comparative study on personalized open-domain generative dialogue systems

Abstract

Developing personalized dialogue systems is a monumental challenge in the rapidly evolving domain of Natural Language Processing (NLP). These systems are engineered to facilitate more natural, intuitive, and user-friendly interactions between humans and machines. Their theoretical significance extends to real-world applications, making them an invaluable asset in today's digital age. Over recent years, this area has garnered immense attention from researchers and academics, becoming a hotbed for innovation and study. This paper aims to contribute to this burgeoning field by conducting an in-depth comparative analysis of the current leading personalized language models. Specifically, this paper will scrutinize models grounded in deep learning algorithms and those that employ reinforcement learning techniques. This research objective is to dissect each approach's unique advantages and limitations. By doing so, the paper hopes to identify actionable avenues for improvement and optimization. Furthermore, the paper will offer a forward-looking perspective, outlining potential advancements and innovations that could shape the future landscape of personalized dialogue systems in Natural Language Processing.