A Study of Text Style Migration Based on Neural Machine Translation
Tao Yang · Theoretical and Natural Science · 2025
With the development of globalization, multilingual processing has emerged as a significant demand. Multilingual text-style migration has several real-world uses, such as improving intercultural communication, encouraging more inclusive multilingual education, and automating content production. These developments help to lower language barriers and promote global connectedness by making texts easier to read and accessible to a wide range of consumers. In the domain of natural language processing, the rapid growth of neural networks has led many scholars to devote themselves to the task of text-style migration using a neural machine translation system, facilitating the conversion of styles between different languages and enhancing the readability of texts. This paper first introduces the history of the development of neural machine translation and common neural machine translation modeling mechanisms. It then categorizes the text-style migration methods based on neural machine translation and specifies the applicable conditions, intrinsic mechanisms, and previous research results of each technique. This overview aims to shed light on the challenges and potential directions in the field of text-style migration, contributing to a deeper understanding of multilingual natural language processing. It is hoped that these revelations will stimulate more investigation and study in this developing field.